Correcting Image Distortion for Adaptive Cruise Control

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鱼眼镜头图像畸变的校正方法(英文)

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0926002-1
第9期
红外与激光工程
第 48 卷
0 Introduction
In many applications of photography, such as machine vision, security monitoring, medical diagnostics, and the projection reality and so on, an imaging lens with very large field angle is often required. A fisheye lens usually satisfies the demands, since it has the field angle of 180° or even larger, and does not need stitching of several pictures without the blind area. However, very serious image distortion exists. In many applications, it is necessary to correct the image distortion of fisheye lens.
收稿日期：2019-04-05 ；修订日期：2019-05-03 基金项目：国家自然科学基金 (11274223) 作者简介：吕丽军 (1963-) ，男，教授，博士生导师，主要从事真空紫外、软 X 射线光学及超大视场光学系统方面的研究。
鱼眼侍业

一种红外图像自适应对比度增强算法

一种红外图像自适应对比度增强算法姚琴芬1，隋修宝2（1. 江苏广播电视大学，江苏南京 210036；2. 南京理工大学，江苏南京 210094）摘要：针对红外图像灰度分布集中、对比度低的特征，提出一种自适应对比度增强算法。

首先，通过统计灰度分布判断目标状态类型；其次，利用目标状态更新图像增强系数；最后，根据增强系数进行灰度去冗余均衡处理。

与传统直方图均衡相比，算法能根据目标强弱状态自适应调整对比度。

实验表明，它在增强目标对比度的同时有效保留了图像整体信息，较好改善了视觉效果。

关键词：红外图像；直方图；自适应；灰度冗余中图分类号：TN 911.73 文献标识码：A 文章编号：1001-8891(2009)09-0541-04 An Adaptive Contrast Enhancement Algorithm for Infrared ImageYAO Qin-fen1，SUI Xiu-bao2(1.Jiangsu Radio & Television University, Nanjing Jiangsu 210036, China；2.Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China)Abstract：Aiming to intensifying gray distribution and low-contrast feature of infrared images, an adaptive contrast enhancement algorithm is proposed. At first, a target state model is presented using gray statistics; then update the enhancing coefficients based on target state model; finally, make an equalization of redundancy eliminating using adjusted coefficients. Comparing to traditional histogram equalization, it can adjust contrast coefficients adaptively using target state. Experiments show that the proposed algorithm can enhance target contrast while keeping image information effectively, and achieve a good visual effect.Key words：infrared image；histogram；adaptive；gray redundancy引言红外成像已广泛应用于军事及民用领域中，然而由于探测器输出信号动态范围很宽，假设实际景物红外辐射的最大变化范围是600K，系统温度分辨率是0.1K，忽略非线性因素影响，则一个典型的红外景象动态范围就有6000个灰度级，而一般的现实系统动态范围远没有这么大，因此，产生了信号处理中源大动态范围和显示小动态范围的矛盾。

绿色对相机的影响英语作文

绿色对相机的影响英语作文Title: The Impact of Green Light on Cameras。

Introduction:In the realm of photography, light is paramount. It shapes our images, dictates exposure, and influences the overall quality of the final product. Among the various hues of light, green light holds a unique position due to its distinct effects on cameras. This essay delves into the impact of green light on cameras, exploring itssignificance and implications.Spectral Composition:Green light, situated within the visible spectrum, occupies wavelengths ranging approximately from 520 to 570 nanometers. Its prevalence in natural landscapes and artificial environments makes it a common element encountered during photography sessions.Exposure and White Balance:The presence of green light significantly affects exposure settings and white balance calibration. Cameras, designed to capture scenes under natural lighting conditions, may struggle to accurately meter and balance green-dominated environments. Consequently, images captured under such conditions may exhibit color casts, inaccuracies in white balance, and compromised exposure levels.Color Representation:Green light influences color reproduction, particularly in scenes where it predominates. Cameras may struggle to faithfully reproduce other hues present in the scene, leading to color shifts and distortion. This phenomenon poses challenges for photographers striving for accurate color rendition in their images.Image Quality:The impact of green light extends beyond mere color representation. It can influence overall image quality, affecting sharpness, contrast, and dynamic range. Excessive green light may result in reduced image clarity and detail, detracting from the visual appeal of photographs.Artistic Considerations:Despite its technical challenges, green light also presents opportunities for creative expression. Photographers adept at harnessing its unique qualities can leverage it to create captivating images imbued with a sense of vitality and vibrancy. By embracing green light as a creative tool rather than a hindrance, photographers can explore new avenues of artistic experimentation.Mitigation Strategies:To mitigate the adverse effects of green light on cameras, photographers employ various techniques and tools. These may include manual adjustment of exposure settings, custom white balance calibration, and the use of lensfilters designed to counteract specific color casts. Additionally, post-processing software offers a plethora of tools for fine-tuning color balance and correcting inaccuracies introduced by green light.Conclusion:In conclusion, the impact of green light on cameras is multifaceted, encompassing technical challenges, artistic opportunities, and mitigation strategies. Understanding its influence enables photographers to navigate diverse shooting environments effectively, ensuring the creation of compelling and technically proficient images. Despite its complexities, green light remains an integral component of the photographic process, shaping the visual narratives captured through the lens.。

高次非球面镜原理

高次非球面镜原理Spherical mirrors are commonly used in many optical systems, but sometimes higher-order aberrations caused by the spherical shape can lead to image distortion. This is where high-order non-spherical mirrors come in, providing a solution to correct these aberrations and improve image quality. 高次非球面镜具有独特的形状，可以有效地纠正球面镜所带来的像差，提高光学系统的成像质量。

One of the key principles behind high-order non-spherical mirrors is the use of more complex surface profiles to minimize aberrations. By carefully designing the shape of the mirror, optical engineers can tailor the mirror to correct specific aberrations and improve overall image quality. 高次非球面镜的关键原理之一是利用更复杂的曲面轮廓来最小化像差。

通过精心设计镜面的形状，光学工程师可以定制镜面以纠正特定的像差，从而提高整体成像质量。

Another important aspect of high-order non-spherical mirrors is their versatility in correcting a wide range of aberrations. Unlike spherical mirrors, which are limited in their ability to correct higher-order aberrations, non-spherical mirrors can be designed to correct avariety of aberrations, making them extremely useful in optical systems. 高次非球面镜的另一个重要方面是它们在纠正各种像差方面的多功能性。

内蒙古农业大学博(硕)士论文样本

学校代码 10129U D C 35.140 学号20022004Quality and Fertility申请人： AAA学科门类：工学学科专业：农业机械化工程研究方向：机器视觉技术在农牧业生产中的应用指导教师： XXX分类号S828 学校代码10129U D C 11.220 学号 20003004鸡毒支原体的分离鉴定及基因免疫的研究Studies on Isolation and Gene Immunizationof Mycoplasma Gallisepticum申请人：BBB学科门类：农学学科专业：基础兽医学研究方向：动物传染病与免疫病理学指导教师：XXX 教授论文提交日期：二〇一三年十二月入孵前种蛋筛选以及种蛋孵化过程中胚胎成活性检测是孵化工作的重要技术环节。

鉴于人工检测劳动强度大，效率低，准确性差。

通过对基于机器视觉的种蛋筛选和孵化成活性检测方法的系统研究，建立了种蛋筛选和孵化成活性自动检测系统。

1．建立了基于机器视觉的种蛋筛选和孵化成活性检测硬件系统。

通过对比试验研究，确定了图像采集时的最佳光源和背景颜色；对种蛋筛选硬件系统进行了标定，标定精度能满足种蛋外观品质检测要求。

2．对基于机器视觉技术的种蛋筛选方法进行了系统研究，建立了种蛋重量、蛋形、蛋壳表面缺陷和蛋壳颜色等4个检测指标的种蛋外观品质综合评价体系。

(1) 提出利用种蛋图像零阶矩计算图像投影面积代替重量称量的方法，检测结果与实际称量值间有良好的相关性，过大蛋、正常蛋、过小蛋检测准确率分别达到了97.73%、97.04%和96.51%。

(2) 研究了基于机器视觉的种蛋蛋壳表面缺陷识别方法，提出利用阈值识别法结合八邻域边界跟踪算法检测裂纹、脏斑、血斑等种蛋蛋壳表面缺陷，裂纹蛋、污斑蛋和正常蛋的检测准确率分别达到了91.25%、94.18%和96.36%。

(3) 提出基于机器视觉、矩和神经网络技术，以种蛋蛋形指数及蛋径差为检测指标的蛋形分步检测方法。

DISTORTION CORRECTING METHOD, DISTORTION CORRECTI

专利名称：DISTORTION CORRECTING METHOD,DISTORTION CORRECTING DEVICE,DISTORTION CORRECTING PROGRAM, ANDDIGITAL CAMERA发明人：MURATA, Tsukasa,OKADA, Sadami申请号：JP2007001445申请日：20071220公开号：WO08/081575P1公开日：20080710专利内容由知识产权出版社提供摘要：A distortion correcting method includes a preparation procedure, a correction procedure, a selection procedure, and a repetition procedure. In the preparation procedure, the relation between a distortion pattern which an optical system gives an image and the lens position of the optical system is acquired. In the correction procedure, by applying data on the lens position to the relation, a distortion pattern of the image obtained by the optical system is deduced. In the correction procedure, distortion correction of the image by means of the distortion correction pattern to suppress the distortion pattern is performed at least once to obtain a temporary corrected image. In the selection procedure, according to the at least one temporary corrected image, a distortion correction pattern to be applied in image correction is selected. In the repetition procedure, when no distortion correction pattern to be applied in image correction is selected, the values included in the data to be applied in the correction procedure are adjusted to repeat the correction procedure.申请人：MURATA, Tsukasa,OKADA, Sadami地址：JP,JP,JP国籍：JP,JP,JP代理机构：FURUYA, Fumio 更多信息请下载全文后查看。

envi的radiometric calibration工具原理 -回复

envi的radiometric calibration工具原理-回复Radiometric calibration is an essential process in remote sensing and satellite imaging, which aims to ensure the accurate measurement of radiometric values from an image. The calibration process corrects any uncertainties or variations caused by sensor characteristics, atmospheric conditions, and other factors. ENVIRadiometric Calibration (ENVIRadCal) is a tool developed by Exelis Visual Information Solutions (Exelis VIS) for radiometric calibration. In this article, we will explore the principles and steps involved in ENVIRadCal.Principle of Radiometric Calibration:Radiometric calibration is based on the concept of converting digital numbers (DN) acquired by a sensor into physical values such as radiance or reflectance. The goal is to establish a quantitative relationship between the digital numbers and the actual radiometric properties of the scene.Step 1: Sensor CharacterizationThe first step in radiometric calibration is to characterize the sensor's response through laboratory measurements. This process is known as sensor calibration. It involves measuring the sensor'sspectral response, linearity, saturation levels, and gain levels under controlled conditions. These measurements are done using calibrated radiation sources and specific calibration techniques. The output of sensor calibration is a set of calibration coefficients that relate the digital numbers acquired by the sensor to the actual radiometric values measured by the calibration devices.Step 2: Atmospheric CorrectionThe next step is atmospheric correction, which accounts for the influence of atmospheric scattering and absorption on the measured radiance. This step is crucial because the atmosphere can significantly affect the radiometric values received by the sensor. Atmospheric correction algorithms estimate and remove the atmospheric effects, allowing an accurate measurement of the surface reflectance or radiance. Several methods are available for atmospheric correction, including models based on physical principles, empirical models, and data-driven methods.Step 3: Calibration ValidationAfter atmospheric correction, the radiometric values are usually compared to ground measurements or other reference data to validate the calibration accuracy. This step helps identify anysystematic biases or errors and allows for fine-tuning the calibration coefficients if necessary. The validation can be performed using field spectroradiometers, ground targets, or reference data from other sensors or satellite missions.Step 4: Image RectificationBefore applying the radiometric calibration to the entire image or dataset, it is necessary to rectify the image to a uniform and consistent coordinate system. Image rectification corrects geometric distortions, such as terrain relief, sensor position and orientation, Earth curvature, and spacecraft motion. This step ensures that each pixel in the image corresponds to a known location on the Earth's surface, facilitating accurate radiometric calibration.Step 5: Applying Radiometric CalibrationOnce the image is rectified, the radiometric calibration coefficients obtained from sensor characterization are applied to the digital numbers of the image. This step converts the calibrated digital numbers into radiometric values such as radiance or reflectance. The radiometric calibration algorithm uses the calibration coefficients to adjust the DN values according to the sensor'sresponse and the atmospheric conditions.Step 6: Evaluation and Quality ControlAfter applying radiometric calibration, it is essential to evaluate the quality of the calibrated image. This evaluation includes assessing various image quality parameters, such as signal-to-noise ratio, radiometric accuracy, spatial resolution, and spectral fidelity. Quality control techniques, such as visual inspection, statistical analysis, and comparison with ground truth data, can be employed to ensure the accuracy and reliability of the radiometric calibration.ENVIRadCal: An OverviewENVIRadCal is a comprehensive radiometric calibration tool provided by Exelis VIS. It seamlessly integrates the above steps with a user-friendly interface, enabling users to perform radiometric calibration in a streamlined manner. ENVIRadCal incorporates sensor-specific calibration information, atmospheric correction algorithms, and image rectification capabilities. It also supports the evaluation of calibration quality and provides advanced visualization tools for result analysis.In conclusion, radiometric calibration is a crucial process in remotesensing and satellite imaging to ensure accurate and reliable radiometric measurements. The ENVIRadCal tool by Exelis VIS provides an effective and efficient solution for performing radiometric calibration. By following the steps outlined above, users can achieve accurate and calibrated radiometric values for their remote sensing applications.。

iDS-2CD7586G0 S-IZHSY 8 MP IR Varifocal Dome网络摄像头说

iDS-2CD7586G0/S-IZHSY8 MP IR Varifocal Dome Network Camera⏹⏹High quality imaging with 8 MP resolution⏹Clear imaging against strong back light due to 120 dB trueWDR technology⏹Efficient H.265+ compression technology to savebandwidth and storage⏹Advanced streaming technology that enables smooth liveview and data self-correcting in poor network⏹ 5 streams and up to 5 custom streams to meet a widevariety of applications⏹Water and dust resistant (IP67) and vandal proof (IK10)⏹Anti-IR reflection bubble guarantees image qualityFunctionHard Hat DetectionWith the embedded deep learning algorithms, the camera detects the persons in the specified region. It detects whether the person is wearing a hard hat, and captures the head of the person and reports an alarm if not.Queue ManagementWith embedded deep learning based algorithms, the camera detects queuing-up people number and waiting time of each person. The body feature detection algorithm helps filter out wrong targets and increase the accuracy of detection. MetadataMetadata uses individual instances of application data or the data content. Metadata can be used for third-party application development.Smooth StreamingSmooth streaming offers solutions to improve the video quality in different network conditions. For example, in poor network conditions, adapting to the detected real-time network condition, streaming bit rate and resolution are automatically adjusted to avoid mosaic and lower latency in live view; in multiplayer network conditions, the camera transmits the redundant data for the self-error correcting in back-end device, so that to solve the mosaic problem because of the packet loss and error rate.Anti-Corrosion CoatingThe camera adopts high weatherproof and wearproof molding powder that confirms to AAMA2604-02 and Qualicoat (Class2) specifications, and uses two-layer special spraying technology, so its surface has excellent long-term anti-corrosion performance and physical protection function. Besides, all the exposed screws use SUS316 material and features highanti-corrosion performance. Therefore, the whole camera meets anti-corrosion requirements NEMA 250-2018.SpecificationCameraImage Sensor 1/2″ Progressive Scan CMOSMin. Illumination Color: 0.009 Lux @ (F1.2, AGC ON); B/W: 0.0009 Lux @ (F1.2, AGC ON) Shutter Speed 1 s to 1/100,000 sSlow Shutter YesP/N P/NWide Dynamic Range 120 dBDay & Night IR cut filterAngle Adjustment Pan: 0° to 355°, tilt: 0° to 75°, rotate: 0° to 355°Power-off Memory YesLensFocus Auto, semi-auto, manual.Lens Type & FOV 2.8 to 12 mm, horizontal FOV 112.3° to 41.2°, vertical FOV 58° to 23.1°, diagonal FOV 137.3° to 47.3°8 to 32 mm, horizontal FOV 41.8° to 14.9°, vertical FOV 22.9° to 8.5°, diagonal FOV 48.7° to 17°Aperture 2.8 to 12mm: F1.2 to F2.5 8 to 32 mm: F1.7 to F1.73Lens Mount IntegratedBlue Glass Module Blue glass module to reduce ghost phenomenon. P-Iris YesIlluminatorIR Range 2.8 to 12 mm: 30 m 8 to 32 mm: 50 mWavelength 850 nm Smart Supplement Light YesVideoMax. Resolution 3840 × 2160Main Stream 50Hz: 25fps (3840 × 2160, 3200 × 1800, 2560 × 1440, 1920 × 1080, 1280 × 720) 60Hz: 30fps (3840 × 2160, 3200 × 1800, 2560 × 1440, 1920 × 1080, 1280 × 720)Sub-Stream 50Hz: 25fps (704 × 576, 640 × 480) 60Hz: 30fps (704 × 480, 640 × 480)Third Stream 50Hz: 25fps (1920 × 1080, 1280 × 720, 704 × 576, 640 × 480) 60Hz: 30fps (1920 × 1080, 1280 × 720, 704 × 480, 640 × 480)Fourth Stream 50Hz: 25fps (1920 × 1080, 1280 × 720, 704 × 576, 640 × 480) 60Hz: 30fps (1920 × 1080, 1280 × 720, 704 × 480, 640 × 480)Fifth Stream 50Hz: 25fps (704 × 576, 640 × 480) 60Hz: 30fps (704 × 480, 640 × 480)Custom Stream 50Hz: 25fps (1920 × 1080, 1280 × 720, 704 × 576, 640 × 480) 60Hz: 30fps (1920 × 1080, 1280 × 720, 704 × 480, 640 × 480)Video Compression Main stream: H.265+/H.265/H.264+/ H.264Sub-stream/Third stream/Fourth stream/Fifth stream/custom stream:H.265/H.264/MJPEGVideo Bit Rate 32 Kbps to 16 MbpsH.264 Type Baseline Profile/Main Profile/High ProfileCBR/VBRStream Type Main stream/Sub-stream/third stream/fourth stream/fifth stream/custom stream Scalable Video Coding (SVC) H.265 and H.264 supportRegion of Interest (ROI) 4 fixed regions for each streamAudioEnvironment Noise Filtering YesAudio Sampling Rate 8 kHz/16 kHz/32 kHz/44.1 kHz/48 kHzAudio Compression G.711/G.722.1/G.726/MP2L2/PCM/MP3Audio Bit Rate 64Kbps(G.711)/16Kbps(G.722.1)/16Kbps(G.726)/32-192Kbps(MP2L2)/32Kbps(PCM)/8-320Kbps(MP3)Audio Type mono soundNetworkSimultaneous Live View Up to 20 channelsAPI Open Network Video Interface (PROFILE S, PROFILE G, PROFILE T), ISAPI, SDK, ISUPProtocols TCP/IP, ICMP, HTTP, HTTPS, FTP, SFTP, DHCP, DNS, DDNS, RTP, RTSP, RTCP, PPPoE, NTP,UPnP, SMTP, SNMP, IGMP, 802.1X, QoS, IPv6, UDP, Bonjour, SSL/TLSSmooth Streaming YesUser/Host Up to 32 users. 3 user levels: administrator, operator and userSecurity Password protection, complicated password, HTTPS encryption, 802.1X authentication (EAP-TLS, EAP-LEAP, EAP-MD5), watermark, IP address filter, basic and digest authentication for HTTP/HTTPS, WSSE and digest authentication for Open Network Video Interface, RTP/RTSP OVER HTTPS, Control Timeout Settings, Security Audit Log, TLS 1.2Network Storage microSD/SDHC/SDXC card (256 GB) local storage, and NAS (NFS, SMB/CIFS), auto network replenishment (ANR)Together with high-end Hikvision memory card, memory card encryption and health detection are supported.Client iVMS-4200, Hik-Connect, Hik-CentralWeb Browser Plug-in required live view: IE8+Plug-in free live view: Chrome 57.0+, Firefox 52.0+, Safari 11+ Local service: Chrome 41.0+, Firefox 30.0+ImageSmart IR The IR LEDs on camera should support Smart IR function to automatically adjust power to avoid image overexposure.Day/Night Switch Day, Night, Auto, Schedule, Triggered by Alarm InTarget Cropping YesDistortion Correction Distortion YesPicture Overlay LOGO picture can be overlaid on video with 128 × 128 24bit bmp format Image Enhancement BLC, HLC, 3D DNR, Defog, EIS, Distortion CorrectionImage Parameters Switch YesImage Settings Saturation, brightness, contrast, sharpness, gain, white balance adjustable by client software or web browserSNR ≥52dBInterfaceAlarm 1 input, 1 output (max. 24 VDC, 1 A)1 input (line in), 3.5 mm connector, max. input amplitude: 3.3 vpp, input impedance:4.7 KΩ, interface type: non-equilibrium;-equilibrium, mono sound; 2 built-inRS-485 1 RS-485(half duplex, HIKVISION, Pelco-P, Pelco-D, self-adaptive)Video Output 1 Vp-p Composite Output(75 Ω/CVBS) (For debugging only) On-board Storage Built-in micro SD/SDHC/SDXC slot, up to 256 GB Hardware Reset YesCommunication Interface 1 RJ45 10 M/100 M/1000 M self-adaptive Ethernet port Power Output 12 VDC, max. 100 mA (supported by all power supply types) Heater YesSmart Feature-SetBasic Event Motion detection, video tampering alarm, vibration detection, exception (network disconnected, IP address conflict, illegal login, HDD full, HDD error)Smart Event Line crossing detection, up to 4 lines configurableIntrusion detection, up to 4 regions configurableRegion entrance detection, up to 4 regions configurableRegion exiting detection, up to 4 regions configurableUnattended baggage detection, up to 4 regions configurableObject removal detection, up to 4 regions configurable,Scene change detection, audio exception detection, defocus detectionCounting Yes Intelligent (Deep Learning Algorithm)Hard Hat Detection Detects up to 30 human targets simultaneously; Supports up to 4 shield regionsPremier Protection Line crossing, intrusion, region entrance, region exitingSupport alarm triggering by specified target types (human and vehicle)Filtering out mistaken alarm caused by target types such as leaf, light, animal, and flag, etc.Queue Management Detects queuing-up people number, and waiting time of each personGenerates reports to compare the efficiency of different queuing-ups and display the changing status of one queueSupports raw data export for further analysisGeneralLinkage Method Upload to FTP/NAS/memory card, notify surveillance center, send email, trigger alarm output, trigger recording, trigger captureTrigger recording: memory card, network storage, pre-record and post-record Trigger captured pictures uploading: FTP, SFTP, HTTP, NAS, EmailTrigger notification: HTTP, ISAPI, alarm output, EmailOnline Upgrade Yes Dual Backup Yes Firmware Version V5.5.121Web Client Language 33 languagesEnglish, Russian, Estonian, Bulgarian, Hungarian, Greek, German, Italian, Czech, Slovak, French, Polish, Dutch, Portuguese, Spanish, Romanian, Danish, Swedish, Norwegian, Finnish, Croatian, Slovenian, Serbian, Turkish, Korean, Traditional Chinese, Thai, Vietnamese, Japanese, Latvian, Lithuanian, Portuguese (Brazil),UkrainianGeneral Function Anti-flicker, 5 streams and up to 5 custom streams, EPTZ, heartbeat, mirror, privacy masks,flash log, password reset via e-mail, pixel counterSoftware Reset YesStorage Conditions -30 °C to 60 °C (-22 °F to 140 °F).Humidity 95% or less (non-condensing) Startup and Operating-40 ° C (-40 °F). Humidity 95% or less (non-condensing)Power Supply12 VDC ± 20%, three-core terminal block, reverse polarity protection; 24 VAC ± 20%, three-core terminal block, reverse polarity protection; PoE: 802.3at, Type 2, Class 4Power Consumption and CurrentWith extra load: 12 VDC, 1.16 A, max. 13.9 W; PoE (802.3at, 42.5 V to 57 V), 0.33 A to 0.25 A, class 4;Without extra load: 12 VDC, 0.98 A, max. 12.5 W; PoE (802.3at, 42.5 V to 57 V), 0.3 A to 0.22 A, class 4;"with extra load" means that an extra device is connected to and powered by the camera.Power Interface Three-core terminal block Camera Material Aluminum alloy bodyCamera Dimension Ø162 × 141.8 mm (Ø6.4″ × 5.6″)Package Dimension 251 × 215 × 189 mm (9.9" × 8.5" × 7.4") Camera Weight1550 g (3.4 lb.) With Package Weight 2206 g (4.9 lb.) Anti-IR reflection bubble YesMetadata Metadata Metadata of intrusion detection, line crossing detection, region entrance detection, region exiting detection, unattended baggage detection, object removal, and queue management are supported.Approval Class Class BEMCFCC (47 CFR Part 15, Subpart B); CE-EMC (EN 55032: 2015, EN 61000-3-2: 2014, EN 61000-3-3: 2013, EN 50130-4: 2011 +A1: 2014); RCM (AS/NZS CISPR 32: 2015); IC (ICES-003: Issue 6, 2016); KC (KN 32: 2015, KN 35: 2015)SafetyUL (UL 60950-1); CB (IEC 60950-1:2005 + Am 1:2009 + Am 2:2013); CE-LVD (EN 60950-1:2005 + Am 1:2009 + Am 2:2013); BIS (IS 13252(Part 1):2010+A1:2013+A2:2015); LOA (IEC/EN 60950-1)Environment CE-RoHS (2011/65/EU); WEEE (2012/19/EU); Reach (Regulation (EC) No 1907/2006) ProtectionIP67 (IEC 60529-2013), IK10 (IEC 62262:2002) Anti-Corrosion Protection NEMA 4X (NEMA 250-2018) OtherPVC free⏹Dimension⏹Available ModeliDS-2CD7586G0/S-IZHSY(2.8-12mm), iDS-2CD7586G0/S-IZHSY(8-32mm)AccessoryDS-1475ZJ-Y Vertical Mount DS-1471ZJ-155-YPendant MountDS-1473ZJ-155-YWall MountDS-1476ZJ-YCorner Mount。

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘要
在竞争激烈的工业自动化生产过程中，机器视觉对产品质量的把关起着举足轻重的作用，机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检测技术相比，自动化的视觉检测系统更加经济、快捷、高效与安全。纹理物体在工业生产中广泛存在，像用于半导体装配和封装底板和发光二极管，现代化电子系统中的印制电路板，以及纺织行业中的布匹和织物等都可认为是含有纹理特征的物体。本论文主要致力于纹理物体的缺陷检测技术研究，为纹理物体的自动化检测提供高效而可靠的检测算法。纹理是描述图像内容的重要特征，纹理分析也已经被成功的应用与纹理分割和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检测算法。这种算法能容忍物体变形引起的图像配准误差，对纹理的影响也具有鲁棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义，如缺陷区域的大小、形状、亮度对比度及空间分布等。同时，在参考图像可行的情况下，本算法可用于同质纹理物体和非同质纹理物体的检测，对非纹理物体的检测也可取得不错的效果。在整个检测过程中，我们采用了可调控金字塔的纹理分析和重构技术。与传统的小波纹理分析技术不同，我们在小波域中加入处理物体变形和纹理影响的容忍度控制算法，来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段，我们检测了一系列具有实际应用价值的图像。实验结果表明本文提出的纹理物体缺陷检测算法具有高效性和易于实现性。关键字: 缺陷检测；纹理；物体变形；可调控金字塔；重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

圆心提取算法综述

圆心提取算法综述摘要：阐述当前主要的圆心提取算法，分析其优缺点和使用范围，为圆心提取算法的运用以及运用中选取合适的圆心提取算法提供参考。

关键字：圆心提取，算法引言：随着现代社会信息技术的飞速发展，特别是20世纪40年代计算机的出现以及50年代人工智能的兴起，人们希望能用计算机来代替或扩展人类的部分脑力劳动，模式识别技术作为人工智能的基础，为满足社会需求获得了快速的发展。

提取圆心是大多数模式识别图像处理与分析的必要环节。

因此，快速而准确的圆心提取算法显得尤为重要。

目前提取圆心的算法主要有以下几种:基于Hough 变换的圆心坐标快速提取方法和一些改进算法 ,平面圆圆心及半径的最小二乘拟合,细化法,正交扫描法、阈值分割提取法、神经网络法等。

本文总结阐述当前主要的圆心提取算法，分析其优缺点和使用范围，为圆心提取算法的运用以及运用中选取合适的圆心提取算法提供参考。

1. 基于Hough 变换的几种圆心提取算法1.1 Hough 变换的基本原理Hough 变换的原理[1] 就是利用图像全局特征将边缘像素连接起来组成区域封闭边界，它将图像空间转换到参数空间，在参数空间对点进行描述，达到检测图像边缘的目的。

该方法把所有可能落在边缘上的点进行统计计算，根据对数据的统计结果确定属于边缘的程度。

Hough 变换的实质就是对图像进行坐标变换，把平面坐标变换为参数坐标，使变换的结果更易识别和检测。

典型的例子就是直线的检测，通过变换图像平面上的点对应到参数平面上的线，而图像平面中的一条直线对应于参数平面中的一簇有公共交点的曲线。

从而检测直线就转换为检测有特殊特征的点，检测到的点，就是图像平面上线的参数。

因此直线就被检测出来了，圆的检测原理类似。

1.2 基本Hough 变换[2,3]已知圆的一般方程为：222)(r b y a x =-+-）（ (1)式中：（a,b ）为圆心，r 为圆的半径。

把- 平面上的圆转换到——参数空间，则图像空间中过任意一点的圆对应于参数空间中的一个三维锥面，图像空间中同一圆上的点对应于参数空间中的所有三维锥面必然交于一点。

数字图像修复的DI灢StyleGANv2模型

第38卷第6期2023年12月安徽工程大学学报J o u r n a l o fA n h u i P o l y t e c h n i cU n i v e r s i t y V o l .38N o .6D e c .2023文章编号:1672-2477(2023)06-0084-08收稿日期:2022-12-16基金项目:安徽省高校自然科学基金资助项目(K J 2020A 0367)作者简介:王坤(1999-),男,安徽池州人,硕士研究生㊂通信作者:张玥(1972-),女,安徽芜湖人,教授,博士㊂数字图像修复的D I -S t yl e G A N v 2模型王坤,张玥*(安徽工程大学数理与金融学院,安徽芜湖 241000)摘要:当数字图像存在大面积缺失或者缺失部分纹理结构时,传统图像增强技术无法从图像有限的信息中还原出更多所需的信息,增强性能有限㊂为此,本文提出了一种将解码信息与S t y l e G A N v 2相融合的深度学习图像修复方法 D I -S t y l e G A N v 2㊂首先由U -n e t 模块生成包含图像主信息的隐编码以及次信息的解码信号,随后利用S t y l e G A N v 2模块引入图像生成先验,在图像生成的过程中不但使用隐编码主信息,并且整合了包含图像次信息的解码信号,从而实现了修复结果的语义增强㊂在F F HQ 和C e l e b A 数据库上的实验结果验证了所提方法的有效性㊂关键词:图像修复;解码信号;生成对抗神经网络;U -n e t ;S t y l e G A N v 2中图分类号:O 29 文献标志码:A 数字图像是现代图像信号最常用的格式,但往往在采集㊁传输㊁储存等过程中,由于噪声干扰㊁有损压缩等因素的影响而发生退化㊂数字图像修复是利用已退化图像中存在的信息重建高质量图像的过程,可以应用于生物医学影像高清化㊁史物影像资料修复㊁军事科学等领域㊂研究数字图像修复模型具有重要的现实意义和商业价值㊂传统的数字图像修复技术多依据图像像素间的相关性和内容相似性进行推测修复㊂在纹理合成方向上,C r i m i n i s i 等[1]提出一种基于图像块复制粘贴的修补方法,该方法采用基于图像块的采样对缺失部分的纹理结构进行信息的填充㊂在结构相似性度研究上,T e l e a [2]提出基于插值的F MM 算法(F a s tM a r c -h i n g M e t h o d );B e r t a l m i o 等[3]提出基于N a v i e r -S t o k e s 方程和流体力学的图像视频修复模型(N a v i e r -S t o k e s ,F l u i dD y n a m i c s ,a n d I m a g e a n dV i d e o I n p a i n t i n g )㊂这些方法缺乏对图像内容和结构的理解,一旦现实中待修复图像的纹理信息受限,修复结果就容易产生上下文内容缺失语义;同时它们需要手动制作同高㊁宽的显示待修复区域的掩码图像来帮助生成结果㊂近年来,由于深度学习领域的快速发展,基于卷积神经网络的图像修复方法得到了广泛的应用㊂Y u等[4]利用空间变换网络从低质量人脸图像中恢复具有逼真纹理的高质量图像㊂除此之外,三维面部先验[5]和自适应空间特征融合增强[6]等修复算法也很优秀㊂这些方法在人工退化的图像集上表现得很好,但是在处理实际中的复杂退化图像时,修复效果依旧有待提升;而类似P i x 2P i x [7]和P i x 2P i x H D [8]这些修复方法又容易使得图像过于平滑而失去细节㊂生成对抗网络[9](G e n e r a t i v eA d v e r s a r i a lN e t w o r k s ,G A N )最早应用于图像生成中,U -n e t 则用于编码解码㊂本文提出的D I -S t y l e G A N v 2模型充分利用S t y l e G A N v 2[10]和U -n e t [11]的优势,通过S t y l e -G A N v 2引入高质量图像生成图像先验,U -n e t 生成修复图像的指导信息㊂在将包含深度特征的隐编码注入预训练的S t y l e G A N v 2中的同时,又融入蕴含图像局部细节的解码信号,得以从严重退化的图像中重建上下文语义丰富且真实的图像㊂1 理论基础1.1 U -n e t U -n e t 是一种A u t o -E n c o d e r ,该结构由一个捕获图像上下文语义的编码路径和能够实现图像重建的对称解码路径组成㊂其前半部分收缩路径为降采样,后半部分扩展路径为升采样,分别对应编码和解码,用于捕获和恢复空间信息㊂编码结构包括重复应用卷积㊁激活函数和用于下采样的最大池化操作㊂在整个下采样的过程中,伴随着特征图尺度的降低,特征通道的数量会增加,扩展路径的解码过程同理,同时扩展路径与收缩路径中相应特征图相连㊂1.2 生成对抗网络生成对抗网络框架由生成器G 和判别器D 这两个模块构成,生成器G 利用捕获的数据分布信息生成图像,判别器D 的输入是数据集的图像或者生成器G 生成的图像,判别器D 的输出刻画了输入图像来自真实数据的可能性㊂为了让生成器G 学习到数据x 的分布p g ,先定义了一个关于输入的噪声变量z ,z 到数据空间的映射表示为G (z ;θg ),其中G 是一个由参数为θg 的神经网络构造的可微函数㊂同时定义了第2个神经网络D (x ;θd ),其输出单个标量D (x )来表示x 来自于数据而不是P g 的概率㊂本训练判别器最大限度地区分真实样本和生成样本,同时训练生成器去最小化l o g(1-D (G (z ))),相互博弈的过程由以下公式定义:m i n G m a x D V (D ,G )=E x ~P d a t a (x )[l o g (D (x ))]+E z ~P z (z )[l o g (1-D (G (z )))],(1)通过生成器㊁判别器的相互对抗与博弈,整个网络最终到达纳什平衡状态㊂这时生成器G 被认为已经学习到了真实数据的内在分布,由生成器合成的生成图像已经能够呈现出与真实数据基本相同的特征,在视觉上难以分辨㊂1.3 S t y l e G A N v 2选用高分辨率图像生成方法S t y l e G A N v 2[10]作为嵌入D I -S t y l e G A N v 2的先验网络,S t yl e G A N v 2[10]主要分为两部分,一部分是映射网络(M a p p i n g N e t w o r k ),如图1a 所示,其中映射网络f 通过8个全连接层对接收的隐编码Z 进行空间映射处理,转换为中间隐编码W ㊂特征空间中不同的子空间信息对应着数据的不同类别信息或整体风格,因为其相互关联存在较高的耦合性,且存在特征纠缠的现象,所以利用映射网络f 使得隐空间得到有效的解耦,最后生成不必遵循训练数据分布的中间隐编码㊂另一部分被称为合成网络,如图1b 所示㊂合成网络由卷积和上采样层构成,其根据映射网络产生的隐编码来生成所需图像㊂合成网络用全连接层将中间隐编码W 转换成风格参数A 来影响不同尺度生成图像的骨干特征,用噪声来影响细节部分使生成的图片纹理更自然㊂S t y l e G A N v 2[10]相较于旧版本重新设计生成器的架构,使用了权重解调(W e i g h tD e m o d u t i o n )更直接地从卷积的输出特征图的统计数据中消除特征图尺度的影响,修复了产生特征伪影的缺点并进一步提高了结果质量㊂图1 S t yl e G A N v 2基本结构根据传入的风格参数,权重调制通过调整卷积权重的尺度替代性地实现对输入特征图的调整㊂w 'i j k =s i ㊃w i jk ,(2)式中,w 和w '分别为原始权重和调制后的权重;s i 为对应第i 层输入特征图对应的尺度㊂接着S t y l e -㊃58㊃第6期王坤,等:数字图像修复的D I -S t y l e G A N v 2模型G A N v 2通过缩放权重而非特征图,从而在卷积的输出特征图的统计数据中消除s i 的影响,经过调制和卷积后,权值的标准偏差为:σj =∑i ,k w 'i j k 2,(3)权重解调即权重按相应权重的L 2范式进行缩放,S t y l e G A N v 2使用1/σj 对卷积权重进行操作,其中η是一个很小的常数以避免分母数值为0:w ″i j k =w 'i j k /∑i ,k w 'i j k 2+η㊂(4)解调操作融合了S t y l e G A N 一代中的调制㊁卷积㊁归一化,并且相比一代的操作更加温和,因为其是调制权重信号,而并非特征图中的实际内容㊂2 D I -S t yl e G A N v 2模型D I -S t y l e G A N v 2模型结构如图2所示:其主要部分由一个解码提取模块U -n e t 以及预先训练过的S t y l e G A N v 2组成,其中S t y l e G A N v 2包括了合成网络部分(S y n t h e s i sN e t w o r k )和判别器部分㊂待修复的输入图像在被输入到整个D I -S t yl e G A N v 2网络之前,需要被双线性插值器调整大小到固定的分辨率1024×1024㊂接着输入图像通过图2中左侧的U -n e t 生成隐编码Z 和解码信号(D e c o d i n g I n f o r m a t i o n ,D I )㊂由图2所示,隐编码Z 经过多个全连接层构成的M a p p i n g Ne t w o r k s 解耦后转化为中间隐编码W ㊂中间隐编码W 通过仿射变换产生的风格参数作为风格主信息,已经在F F H Q 数据集上训练过的S t yl e G A N v 2模块中的合成网络在风格主信息的指导下恢复图像㊂合成网络中的S t y l e G A N B l o c k 结构在图2下方显示,其中包含的M o d 和D e m o d 操作由式(2)和式(4)给出㊂同时U -n e t 解码层每一层的解码信息作为风格次信息以张量拼接的方式加入S t y l e G A NB l o c k 中的特征图,更好地让D I -S t y le G A N v 2模型生成细节㊂最后将合成网络生成的图片汇入到判别器中,由判别器判断是真实图像还是生成图像㊂图2 数字图像修复网络结构图模型鉴别器综合了3个损失函数作为总损失函数:对抗损失L A ㊁内容损失L C 和特征匹配损失L F ㊂其中,L A 为原始G A N 网络中的对抗损失,被定义为式(5),X 和^X 表示真实的高清图像和低质量的待修复图像,G 为训练期间的生成器,D 为判别器㊂L A =m i n G m a x D E (X )L o g (1+e x p (-D (G (X ^)))),(5)L C 定义为最终生成的修复图片与相应的真实图像之间的L 1范数距离;L F 为判别器中的特征层,其定义为式(6);T 为特征提取的中间层总数;D i (X )为判别器D 的第i 层提取的特征:㊃68㊃安徽工程大学学报第38卷L F =m i n G E (X )(∑T i =0||D i (X )-D i (G (X ^))||2),(6)最终的总损失函数为:L =L A +αL C +βL F ,(7)式(7)中内容损失L C 判断修复结果和真实高清图像之间的精细特征与颜色信息上差异的大小;通过判别器中间特征层得到的特征匹配损失L F 可以平衡对抗损失L A ,更好地恢复㊁还原高清的数字图像㊂α和β作为平衡参数,在本文的实验中根据经验设置为α=1和β=0.02㊂3实验结果分析图3 F F HQ 数据集示例图3.1 数据集选择及数据预处理从图像数据集的多样性和分辨率考虑,本文选择在F F H Q (F l i c k rF a c e s H i g h Q u a l i t y )数据集上训练数字图像修复模型㊂该数据集包含7万张分辨率为1024×1024的P N G 格式高清图像㊂F F H Q 囊括了非常多的差异化的图像,包括不同种族㊁性别㊁表情㊁脸型㊁背景的图像㊂这些丰富属性经过训练可以为模型提供大量的先验信息,图3展示了从F F H Q 中选取的31张照片㊂训练过程中的模拟退化过程,即模型生成数据集对应的低质量图像这部分,本文主要通过以下方法实现:通过C V 库对图像随机地进行水平翻转㊁颜色抖动(包括对图像的曝光度㊁饱和度㊁色调进行随机变化)以及转灰度图等操作,并对图像采用混合高斯模糊,包括各向同性高斯核和各向异性高斯核㊂在模糊核设计方面,本文采用41×41大小的核㊂对于各向异性高斯核,旋转角度在[-π,π]之间均匀采样,同时进行下采样和混入高斯噪声㊁失真压缩等处理㊂整体模拟退化处理效果如图4所示㊂图4 模拟退化处理在模型回测中,使用C e l e b A 数据集来生成低质量的图像进行修复并对比原图,同时定量比较本模型与近年来提出的其他方法对于数字图像的修复效果㊂3.2 评估指标为了公平地量化不同算法视觉质量上的优劣,选取图像质量评估方法中最广泛使用的峰值信噪比(P e a kS i g n a l -t o -n o i s eR a t i o ,P S N R )以及结构相似性指数(S t r u c t u r a l S i m i l a r i t y I n d e x ,S S I M )指标,去量化修复后图像和真实图像之间的相似性㊂P S N R 为信号的最大可能功率和影响其精度的破坏性噪声功率的比值,数值越大表示失真越小㊂P S N R 基于逐像素的均方误差来定义㊂设I 为高质量的参考图像;I '为复原后的图像,其尺寸均为m ×n ,那么两者的均方误差为:M S E =1m n ∑m i =1∑n j =1(I [i ,j ]-I '[i ,j ])2,(8)P S N R 被定义为公式(9),P e a k 表示图像像素强度最大的取值㊂㊃78㊃第6期王坤,等:数字图像修复的D I -S t yl e G A N v 2模型P S N R =10×l o g 10(P e a k 2M S E )=20×l o g 10(P e a k M S E ),(9)S S I M 是另一个被广泛使用的图像相似度评价指标㊂与P S N R 评价逐像素的图像之间的差异,S S I M 仿照人类视觉系统实现了其判别标准㊂在图像质量的衡量上更侧重于图像的结构信息,更贴近人类对于图像质量的判断㊂S S I M 用均值估计亮度相似程度,方差估计对比度相似程度,协方差估计结构相似程度㊂其范围为0~1,越大代表图像越相似;当两张图片完全一样时,S S I M 值为1㊂给定两个图像信号x 和y ,S S I M 被定义为:S S I M (x ,y )=[l (x ,y )α][c (x ,y )]β[s (x ,y )]γ,(10)式(10)中的亮度对比l (x ,y )㊁对比度对比c (x ,y )㊁结构对比s (x ,y )三部分定义为:l (x ,y )=2μx μy +C 1μ2x +μ2y +C 1,c (x ,y )=2σx σy +C 2σ2x +σ2y +C 2,l (x ,y )=σx y +C 3σx σy +C 3,(11)其中,α>0㊁β>0㊁γ>0用于调整亮度㊁对比度和结构之间的相对重要性;μx 及μy ㊁σx 及σy 分别表示x 和y 的平均值和标准差;σx y 为x 和y 的协方差;C 1㊁C 2㊁C 3是常数,用于维持结果的稳定㊂实际使用时,为简化起见,定义参数为α=β=γ=1以及C 3=C 2/2,得到:S S I M (x ,y )=(2μx μy +C 1)(2σx y +C 2)(μ2x +μ2y +C 1)(σ2x +σ2y +C 2)㊂(12)在实际计算两幅图像的结构相似度指数时,我们会指定一些局部化的窗口,计算窗口内信号的结构相似度指数㊂然后每次以像素为单位移动窗口,直到计算出整幅的图像每个位置的局部S S I M 再取均值㊂3.3 实验结果(1)D I -S t y l e G A N v 2修复结果㊂图5展示了D I -S t yl e G A N v 2模型在退化图像上的修复结果,其中图5b ㊁5d 分别为图5a ㊁5c 的修复结果㊂通过对比可以看到,D I -S t y l e G A N v 2修复过的图像真实且还原,图5b ㊁5d 中图像的头发㊁眉毛㊁眼睛㊁牙齿的细节清晰可见,甚至图像背景也被部分地修复,被修复后的图像通过人眼感知,整体质量优异㊂图5 修复结果展示(2)与其他方法的比较㊂本文用C e l e b A -H Q 数据集合成了一组低质量图像,在这些模拟退化图像上将本文的数字图像修复模型与G P E N [12]㊁P S F R G A N [13]㊁H i F a c e G A N [14]这些最新的深度学习修复算法的修复效果进行比较评估,这些最近的修复算法在实验过程中使用了原作者训练过的模型结构和预训练参数㊂各个模型P S N R 和L P I P S 的测试结果如表1所示,P S N R 和S S I M 的值越大,表明修复图像和真实高清图像之间的相似度越高,修复效果越好㊂由表1可以看出,我们的数字图像修复模型获得了与其他顶尖修复算法相当的P S N R 指数,L P I P S 指数相对于H i F a c e G A N 提升了12.47%,同时本实验环境下修复512×512像素单张图像耗时平均为1.12s ㊂表1 本文算法与相关算法的修复效果比较M e t h o d P S N R S S I M G P E N 20.40.6291P S F R G A N 21.60.6557H i F a c e G A N 21.30.5495o u r 20.70.6180值得注意的是P S N R 和S S I M 大小都只能作为参考,不能绝对反映数字图像修复算法的优劣㊂图6展示了D I -S t y l e G A N v 2㊁G P E N ㊁P S F R G A N ㊁H i F a c e G A N 的修复结果㊂由图6可以看出,无论是全局一致性还是局部细节,D I -S t y l e G A N v 2都做到了很好得还原,相比于其他算法毫不逊色㊂在除人脸以外的其他自然场景的修复上,D I -S t yl e G A N v 2算法依旧表现良好㊂图7中左侧为修复前㊃88㊃安徽工程大学学报第38卷图像,右侧为修复后图像㊂从图7中红框处标记出来的细节可以看出,右侧修复后图像的噪声相比左图明显减少,观感更佳,这在图7下方放大后的细节比对中表现得尤为明显㊂从图像中招牌的字体区域可见对比度和锐化程度的提升使得图像内部图形边缘更加明显,整体更加清晰㊂路面的洁净度上噪声也去除很多,更为洁净㊂整体上修复后图像的色彩比原始的退化图像要更丰富,层次感更强,视觉感受更佳㊂图6 修复效果对比图图7 自然场景图像修复结果展示(左:待修复图像;右:修复后图像)4 结论基于深度学习的图像修复技术近年来在超分辨图像㊁医学影像等领域得到广泛的关注和应用㊂本文针对传统修复技术处理大面积缺失或者缺失部分纹理结构的图像时容易产生修复结果缺失图像语义的问题,在国内外图像修复技术理论与方法的基础上,由卷积神经网络U -n e t 结合近几年效果极佳的生成对抗网络S t yl e G A N v 2,提出了以图像解码信息㊁隐编码㊁图像生成先验这三类信息指导深度神经网络对图像进行修复㊂通过在F F H Q 数据集上进行随机图像退化模拟来训练D I -S t y l e G A N v 2网络模型,并由P S N R 和S S I M 两个指标来度量修复样本和高清样本之间的相似性㊂㊃98㊃第6期王坤,等:数字图像修复的D I -S t y l e G A N v 2模型㊃09㊃安徽工程大学学报第38卷实验表明,D I-S t y l e G A N v2网络模型能够恢复清晰的面部细节,修复结果具有良好的全局一致性和局部精细纹理㊂其对比现有技术具有一定优势,同时仅需提供待修复图像而无需缺失部分掩码就能得到令人满意的修复结果㊂这主要得益于D I-S t y l e G A N v2模型能够通过大样本数据的训练学习到了丰富的图像生成先验,并由待修复图像生成的隐编码和解码信号指导神经网络学习到更多的图像结构和纹理信息㊂参考文献:[1] A N T O N I O C,P A T R I C KP,K E N T A R O T.R e g i o n f i l l i n g a n do b j e c t r e m o v a l b y e x e m p l a r-b a s e d i m a g e i n p a i n t 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m a s sV i l l a g e:I E E EC o m p u t e r S o c i e-t y,2020:8110-8119.[11]O L A FR O N N E B E R G E R,P H I L I P PF I S C H E R,T HOMA SB R O X.U-n e t:c o n v o l u t i o n a l n e t w o r k s f o r b i o m e d i c a l i m a g es e g m e n t a t i o n[C]//M e d i c a l I m a g eC o m p u t i n g a n dC o m p u t e r-A s s i s t e dI n t e r v e n t i o n-M I C C A I2015:18t hI n t e r n a t i o n a lC o n f e r e n c e.M u n i c h:S p r i n g e r I n t e r n a t i o n a l P u b l i s h i n g,2015:234-241.[12]Y A N G T,R E NP,X I EX,e t a l.G a n p r i o r e m b e d d e dn e t w o r k f o r b l i n d f a c e r e s t o r a t i o n i n t h ew i l d[C]//P r o c e e d i n g s o ft h eI E E E/C V F C o n f e r e n c eo n C o m p u t e r V i s i o na n d P a t t e r n R e c o g n i t i o n.V i r t u a l:I E E E C o m p u t e rS o c i e t y,2021: 672-681.[13]C H E NCF,L IX M,Y A N GLB,e t a l.P r o g r e s s i v e s e m a n t i c-a w a r e s t y l e t r a n s f o r m a t i o n f o r b l i n d f a c e r e s t o r a t i o n[C]//P r o c e e d i n g s o f t h e I E E E/C V FC o n f e r e n c e o nC o m p u t e rV i s i o n a n dP a t t e r nR e c o g n i t i o n.V i r t u a l:I E E EC o m p u t e r S o c i-e t y,2021:11896-11905.[14]Y A N GLB,L I U C,WA N GP,e t a l.H i f a c e g a n:f a c e r e n o v a t i o nv i a c o l l a b o r a t i v e s u p p r e s s i o na n dr e p l e n i s h m e n t[C]//P r o c e e d i n g s o f t h e28t hA C MI n t e r n a t i o n a l C o n f e r e n c e o n M u l t i m e d i a.S e a t t l e:A s s o c i a t i o n f o rC o m p u t i n g M a c h i n e r y, 2020:1551-1560.D I -S t y l e G A N v 2M o d e l f o rD i g i t a l I m a geR e s t o r a t i o n WA N G K u n ,Z H A N G Y u e*(S c h o o l o fM a t h e m a t i c s a n dF i n a n c e ,A n h u i P o l y t e c h n i cU n i v e r s i t y ,W u h u241000,C h i n a )A b s t r a c t :W h e n t h e r e a r e l a r g e a r e a s o fm i s s i n g o r c o m p l e x t e x t u r e s t r u c t u r e s i n d i g i t a l i m a g e s ,t r a d i t i o n -a l i m a g e e n h a n c e m e n t t e c h n i q u e s c a n n o t r e s t o r em o r e r e q u i r e d i n f o r m a t i o n f r o mt h e l i m i t e d i n f o r m a t i o n i n t h e i m a g e ,r e s u l t i n g i n l i m i t e d e n h a n c e m e n t p e r f o r m a n c e .T h e r e f o r e ,t h i s p a p e r p r o p o s e s a d e e p l e a r n -i n g i n p a i n t i n g m e t h o d ,D I -S t y l e G A N v 2,w h i c hc o m b i n e sd e c o d i n g i n f o r m a t i o n (D I )w i t hS t y l e G A N v 2.F i r s t l y ,t h eU -n e tm o d u l e g e n e r a t e s ah i d d e n e n c o d i n g s i g n a l c o n t a i n i n g t h em a i n i n f o r m a t i o no f t h e i m -a g e a n d a d e c o d i n g s i g n a l c o n t a i n i n g t h e s e c o n d a r y i n f o r m a t i o n .T h e n ,t h e S t y l e G A N v 2m o d u l e i s u s e d t o i n t r o d u c e a n i m a g e g e n e r a t i o n p r i o r .D u r i n g t h e i m a g e g e n e r a t i o n p r o c e s s ,n o t o n l y t h eh i d d e ne n c o d i n gm a i n i n f o r m a t i o n i s u s e d ,b u t a l s o t h e d e c o d i n g s i g n a l c o n t a i n i n g t h e s e c o n d a r y i n f o r m a t i o no f t h e i m a g e i s i n t e g r a t e d ,t h e r e b y a c h i e v i n g s e m a n t i c e n h a n c e m e n t o f t h e r e p a i r r e s u l t s .T h e e x pe r i m e n t a l r e s u l t s o n F F H Qa n dC e l e b Ad a t a b a s e s v a l i d a t e t h e ef f e c t i v e n e s s o f t h e p r o p o s e da p p r o c h .K e y w o r d s :i m ag e r e s t o r a t i o n ;d e c o d e s i g n a l ;g e n e r a t i v e a d v e r s a r i a l n e t w o r k ;U -n e t ;S t y l e G A N v 2(上接第83页)P r o g r e s s i v e I n t e r p o l a t i o nL o o p S u b d i v i s i o n M e t h o d w i t hD u a lA d ju s t a b l eF a c t o r s S H IM i n g z h u ,L I U H u a y o n g*(S c h o o l o fM a t h e m a t i c s a n dP h y s i c s ,A n h u i J i a n z h uU n i v e r s i t y ,H e f e i 230601,C h i n a )A b s t r a c t :A i m i n g a t t h e p r o b l e mt h a t t h e l i m i ts u r f a c e p r o d u c e db y t h ea p p r o x i m a t eL o o p s u b d i v i s i o n m e t h o d t e n d s t o s a g a n d s h r i n k ,a p r o g r e s s i v e i n t e r p o l a t i o nL o o p s u b d i v i s i o nm e t h o dw i t hd u a l a d j u s t a -b l e f a c t o r s i s p r o p o s e d .T h i sm e t h o d i n t r o d u c e s d i f f e r e n t a d j u s t a b l e f a c t o r s i n t h e t w o -p h a s eL o o p s u b d i -v i s i o nm e t h o d a n d t h e p r o g r e s s i v e i t e r a t i o n p r o c e s s ,s o t h a t t h e g e n e r a t e d l i m i t s u r f a c e i s i n t e r p o l a t e d t o a l l t h e v e r t i c e s o f t h e i n i t i a l c o n t r o lm e s h .M e a n w h i l e ,i t h a s a s t r i n g e n c y ,l o c a l i t y a n d g l o b a l i t y .I t c a nn o t o n l y f l e x i b l y c o n t r o l t h e s h a p e o f l i m i t s u r f a c e ,b u t a l s o e x p a n d t h e c o n t r o l l a b l e r a n g e o f s h a p e t o a c e r -t a i ne x t e n t .F r o mt h e n u m e r i c a l e x p e r i m e n t s ,i t c a nb e s e e n t h a t t h em e t h o d c a n r e t a i n t h e c h a r a c t e r i s t i c s o f t h e i n i t i a l t r i a n g u l a rm e s hb e t t e rb y c h a n g i n g t h ev a l u eo f d u a l a d j u s t a b l e f a c t o r s ,a n d t h e g e n e r a t e d l i m i t s u r f a c eh a s a s m a l l d e g r e e o f s h r i n k a g e ,w h i c h p r o v e s t h em e t h o d t ob e f e a s i b l e a n d e f f e c t i v e .K e y w o r d s :L o o p s u b d i v i s i o n ;p r o g r e s s i v e i n t e r p o l a t i o n ;d u a l a d j u s t a b l e f a c t o r s ;a s t r i n g e n c y ㊃19㊃第6期王坤,等:数字图像修复的D I -S t y l e G A N v 2模型。

大型光学红外望远镜拼接非球面子镜反衍补偿检测光路设计

大型光学红外望远镜拼接非球面子镜反衍补偿检测光路设计王丰璞李新南徐晨黄亚Optical testing path design for LOT aspheric segmented mirrors with reflective-diffractive compensationWANG Feng-pu, LI Xin-nan, XU Chen, HUANG Ya引用本文:王丰璞,李新南,徐晨,黄亚. 大型光学红外望远镜拼接非球面子镜反衍补偿检测光路设计[J]. 中国光学, 2021, 14(5): 1184-1193. doi: 10.37188/CO.2020-0218WANG Feng-pu, LI Xin-nan, XU Chen, HUANG Ya. Optical testing path design for LOT aspheric segmented mirrors with reflective-diffractive compensation[J]. Chinese Optics, 2021, 14(5): 1184-1193. doi: 10.37188/CO.2020-0218在线阅读 View online: https:///10.37188/CO.2020-0218您可能感兴趣的其他文章Articles you may be interested in非零位凸非球面子孔径拼接检测技术研究Research on non-null convex aspherical sub-aperture stitching detection technology中国光学. 2018, 11(5): 798 https:///10.3788/CO.20181105.0798易测量非球面定义及应用Definition and application of easily measurable aspheric surfaces中国光学. 2017, 10(2): 256 https:///10.3788/CO.20171002.0256大偏离度非球面检测畸变校正方法Distortion correcting method when testing large-departure asphere中国光学. 2017, 10(3): 383 https:///10.3788/CO.20171003.0383基于单次傅里叶变换的分段衍射算法Step diffraction algorithm based on single fast Fourier transform algorithm中国光学. 2018, 11(4): 568 https:///10.3788/CO.20181104.0568一种针对超大口径凸非球面的面形检测方法Surface testing method for ultra-large convex aspheric surfaces中国光学. 2019, 12(5): 1147 https:///10.3788/CO.20191205.1147超颖表面原理与研究进展The principle and research progress of metasurfaces中国光学. 2017, 10(5): 523 https:///10.3788/CO.20171005.0523文章编号 2095-1531（2021）05-1184-10大型光学红外望远镜拼接非球面子镜反衍补偿检测光路设计王丰璞1,2,3，李新南1,2 *，徐　晨1,2，黄　亚1,2（1. 中国科学院国家天文台南京天文光学技术研究所，江苏南京 210042；2. 中国科学院天文光学技术重点实验室 (南京天文光学技术研究所)，江苏南京 210042；3. 中国科学院大学，北京 100049）摘要：为了实现大口径、长焦距、批量化离轴镜面的高精度面形检验，本文提出了一种零位反衍补偿检测方案，采用计算全息和球面反射镜共同对离轴镜面法向像差进行补偿，检测光路波像差残差接近于零。

Rolling shutter distortion correction

Rolling shutter distortion correctionChia-Kai Liang*, Yu-Chun Peng**, and Homer Chen***Graduate Institute of Communication Engineering, National Taiwan University, TaiwanABSTRACTAs opposed to the global shutter, which starts and stops the light integration of each pixel at the same time by incorporating a sample-and-hold switch with analog storage in each pixel, the electronic rolling shutter found in most low-end CMOS image sensors today collects the image data row by row, analogous to an open slit that scans over the image sequentially. Each row integrates the light when the slit passes over it. Therefore, the scanlines of the image are not exposed at the same time. This sensor architecture creates an objectionable geometric distortion, known as the rolling shutter effect, for moving objects. In this paper, we address this problem by using digital image processing techniques. A mathematical model of the rolling shutter is developed. The relative image motion between the moving objects and the camera is determined by block-based motion estimation. A Bezier curve fitting is applied to smooth the resulting motion data , which are then used for the alignment of scanlines. The basic ideas behind the algorithm presented here can be generalized to deal with other complicated cases.Keywords: rolling shutter, motion estimation1.INTRODUCTIONMore and more low-cost imaging devices are equipped with CMOS image sensor array. Unlike the CCD sensor array that normally has interline connections, the CMOS sensor array does not hold and store all the pixels. An electronic rolling shutter is thus resulted because each scanline of the CMOS sensor array is exposed, sampled, and stored sequentially. An annoying effect of the electronic rolling shutter is that it introduces geometric distortion to moving objects. A graphical illustration of this annoying effect is shown in Figure 1. We can see that the image of a vertical object becomes slanted. In this simple example, we assume that the object is stationary and the camera is moving. Note that the same effect occurs when the image of a moving object is captured by a still camera. In general, as long as there is a relative motion between the camera the scene, the rolling shutter effect would appear. The extent of the geometrical distortion depends on the magnitude and direction of the relative motion.Many circuit architectures have been developed to address the rolling shutter problem1, 2 by implementing a sample-and-hold circuit for the CMOS sensor. However, the transistor count of such architectures becomes two to four times more than that of the original architecture, introducing new problems, such as the requirement of more advanced IC process, that are more costly to solve.Little has been studied to overcome the rolling shutter effect by digital image processing. C. Geyer et al. have derived a camera model for rolling shutter cameras3. They developed a general rolling-shutter constraint on the motion of each image point. However, this approach involves an elaborated procedure to compute the 3-D motion of object that is sensitive to image errors..In this paper, we develop a simple model to characterize the rolling shutter effect without resorting to 3-D analysis. With this model, the rolling shutter distortion can be corrected by the alignment of scanlines. An attractive feature of this algorithm is that it is relatively easy to implement.The paper is organized as follows. Section 2 describes our approach to the compensation of rolling shutter distortion, including a basic mathematical model of the rolling shutter, an algorithm for rolling shutter compensation, and the extension of the algorithm to more general image motions . Section 3 describes the setup of our experiments in detail and shows the simulation results. This is followed by a conclusion in Section 4.*r93942031@.tw **b88901049@.tw ***homer@.twVisual Communications and Image Processing 2005, edited by Shipeng Li,Fernando Pereira, Heung-Yeung Shum, Andrew G. Tescher, Proc. of SPIE Vol. 5960(SPIE, Bellingham, WA, 2005) · 0277-786X/05/$15 · doi: 10.1117/12.6326712.APPROACHWithout loss of generality, we start the derivation of our approach with the case where the object is stationary and thecamera moves horizontally from right to left, as shown in Figure 1(a). Since the camera is moving, by the time thecamera finishes storing the sampled data of the first scanline and is ready to scan the second line, the object wouldappear to have moved by a certain distance horizontally. Therefore, a displacement between the sampled data along thetwo scanlines is resulted. This is shown in Figure 1(b). In effect, the shape of the object is seriously distorted whenthe entire image of the object is captured. The basic idea of our approach, as shown in Figure 2(a), is to correct thisgeometric distortion by aligning the scanlines according to the apparent object movement detected in the image. InFigure 2(a), x i is the displacement between scanlines l i and l i+1. Note that x i is equivalent to the displacement between the horizontal object segments shown in Figure 2(b).(a) (b) Figure 1. Illustration of the rolling shutter effect. (a) A vertical object appears to move to the left of the image while a rolling shuttercamera moving horizontally to the right of the object acquires the image data line-by-line. (b) The resulting image of the verticalobject (partial view). Note the inevitable distortion on the object caused by the rolling shutter.(a) (b)Figure 2. (a) Correcting the rolling shutter distortion by aligning the scanlines. (b) The object displacement x i betweenscanlines.For the approach to work, the displacement (or offset) x i along each scanline has to be determined. This displacementof scaneline l i is related to the velocity v(t) of the object, measured in the image plane, by1,nT i ti nT i t x v t dt ' ' ³ (1)where n is the frame index, Ϧt is the exposure time of each scanline, and T is the time interval between two consecutivevideo frames. For most commercial cameras, we may consider that there is a constant time lapse d between the end of theprevious frame and the beginning of the current frame. Assuming a constant frame rate f , we have T=1/f . Then theexposure time of the scaneline can be determined by,t T d S '(2) where S denotes the total number of effective scanlines in a video frame.Based on v (t ), we can determine the displacement x i line-by-line and hence compensate the rolling shutter distortion.Since the object is stationary, we determine v (t ) by global motion estimation. The global motion vector of the n th frameis related to the camera motion by³nT T n dt t v nT GMV 1 (3) ³¦ nT n i dt t v iT GMV nT AGMV 00 (4)where GMV (nT ) is the global motion vector of the n th frame and AGMV (nT ) denotes the accumulated global motion vector with respect to the first frame.If v (t ) varies slowly, it can be computed by the finite difference method as followsT nT GMV T T n AGMV nT AGMV dt t dAGMV nT v nT t 1)( (5)The block diagram of our rolling shutter correction algorithm is shown in Figure 3. In this algorithm, a block-based motion estimation is applied to the image sequence. The global motion is determined by clustering and classifying the motion vectors thus obtained.5 Then v (t ) is calculated by equation (5), and x i is obtained by (1). Finally each scanline of the distorted image is compensated by x ito generate the corrected image.Figure 3.Block diagram of the rolling shutter correction algorithm.Note that a constant velocity for the entire frame is assumed in (5). However, this constant velocity assumption may not always be a good approximation. This is illustrated in Figure 5, where the solid curve is the actual accumulated global motion, and the piecewise linear segment represents the accumulated global motion under the constant velocity assumption. An ideal case occurs when the two curves completely overlap. In such a case, the estimated x i leads to accurate rolling shutter compensation. When the two curves are apart from each other during a frame interval, the algorithm may not generate good result.To improve the performance of the algorithm, we reconstruct the value of AGMV (t ) from the sampled AGMV (nT )and obtain dAGMV (t )/dt at the time instant of interest. Here, we are dealing with the case where the sampling rate of AGMV (t ), which is equal to the frame rate, is potentially smaller than the bandwidth of AGMV (t ). According to the Nyquist theorem, this may lead to an aliasing effect. If the reconstructed AGMV (t ) is larger than the real one, an over-compensated frame is resulted, as shown in the 45th compensated frame in Figure 4(b) where the Christmas tree is slightly tilted over to the left. Note that the over-compensation can happen to the finite difference method and thereconstruction method.(a)(b)Figure 4. (a) The 39th (left) and 45th (right) frames of the original sequence (6 frames/sec). (b) The corrected frames.Figure 5. Accumulated Global Motion Vector.We have observed the over-compensation degrade the video quality much seriously than under-compensation. Therefore, we need to apply a low pass filter upon AGMV (t ) to make it smoother. However, it is difficult to find a filter suitable for AGMV (t ). Even it can be found, it normally requires a large number of samples as input, resulting in a large output delay. Instead, we adopt the Bezier method to approximate the curve. A Bezier curve for AGMV (t ) between nT and (n +1)T is described by,1313143322213P t P t t P t t P t t Q (6)where P i ’s are the control points of the curve, which can be derived by using the following boundary conditionsT n AGMV Q 20 (7) T n AGMV Q 11 (8) nT AGMV Q 32 (9)T n AGMV Q 11 (10)Once the control points are obtained, the velocity v (t ) can be calculated by ¸¹·¨©§ T T t Q dt d t v 32 (11)The dotted curve in Figure 5 shows the reconstructed curve based on AGMV(nT). The Bezier method removes the high frequency components of AGMV(t). Unlike deterministic low pass filters, the Bezier method is a low pass filter with adaptive control points and introduces only one frame delay. The control points are updated frame-by-frame according to the local characteristics of AGMV(t). The result of Bezier curve method is shown in Figure 6, where the over-compensation is removed.(a)(b)Figure 6. (a) The 39th (left) and 45th (right) frames of the original sequence (6 frames/sec). (b) The corrected frames using Bezier curve approximation.We have shown how to correct the rolling shutter distortion for the simple horizontal motion case. Now we discuss how to generalize it to other types of motions. For the case where the scene is static and the camera moves perpendicularly to the scanlines, the rolling shutter compensation can be achieved in a similar way. However, an extra step is required to interpolate the missing scanlines. An illustration of the problem is shown in Figure 7.(a) (b)Figure 7. The rolling shutter effect due to vertical camera motion. (a) The distorted image. (b) The compensated image.If the objects in the scene are at different depths, our algorithm in general can still restore the image because the global motion obtained by the algorithm represents the motion of most parts of the image. For the case where the camera is stationary and the objects are moving, the algorithm can be further enhanced by supplementing it with a segmentation technique to distinguish the objects from each other and from the background.Figure 8. VGA frame timing of OmniVision 1.3 MegaPixel CameraChip3.SIMULATION RESULTSWe implement our algorithm on two different video sequences captured by OmniVision 1.3 MegaPixel CameraChip4. This chip only performs image format conversion and color interpolation on the captured images. The timing parameters of this chip shown in Figure 8 are obtained from the user’s manual. Both simulation sequences are in VGA size. In our simulations, the search range of motion estimation is r120 pixels and the vertical compensation is turned off.Figures 9 and 10 show the results of our first simulation. The frame rate of this sequence is 6 frames per second. The blank region of the corrected images is filled with black pixels. Based on the black region, it can be clearly seen that the velocity v(t) of (1) is not constant.Figure 9. The original and corrected 1st, 5th and 6th, and frames of the first sequenceFigure 10. The original and corrected 12th, 26th, and 38th frames of the second sequence Figures 11 and 12 show the simulation result of our second sequence. In this simulation, the frame rate is 12 frames per second. Because of the smoothing effect on the global motion vectors, no over-compensation occurs in the corrected images.Figure 11. The original and corrected 1st, 15th, and 30th frames of the second sequenceFigure 12. The original and corrected 45th, 60th , and 75th frames of the second sequence Our algorithm corrects most distortions caused by translational movements, even if there are moving objects in the scene. However, non-translational movements, such as rotation and zooming, are not analyzed yet. The information loss in non-translational motion is serious and block-based motion estimation is not suitable to detect such movements. We will construct a more robust distortion model of the rolling shutter effect in non-translational motion.4.CONCLUSIONIn this paper, we have described an algorithm for compensating the rolling shutter effect. A mathematical model for rolling shutter cameras is developed. Two different correction methods, the finite difference method and the reconstruction method, are examined and their drawbacks are discussed. Because over-compensation degrades the video quality, a Bezier curve fitting technique is adopted to smooth the global motion and improve the video quality. Compared with other techniques, this algorithm only involves digital image processing and does not need any sample-and-hold circuit. The algorithm is relative easy to implement.REFERNECES1.S. Lauxtermann et al., “A mega-pixel high speed CMOS imager with sustainable gigapixel/sec readout rate”, inProc. 2001 IEEE Workshop on Charge-Coupled Devices and Advanced Image Sensors, pp. 48-51, Lake Tahoe, NV, June 7-9, 20012.M. Wäny and G.Paul, “CMOS image sensor with NMOS-only global shutter and enhanced responsivity”, in IEEETransactions on Electron Devices, vol. 50, no. 1, pp. 57-62, January, 20033. C. Geyer, M. Meingast and S. Sastry, “Geometric Models of Rolling-Shutter Cameras”, 4.OmniVision Technologies, Inc. 5.Chia-Kai Liang, Yu-Chun Peng, Hung-Au Chang, Che-Chun Su and Homer Chen, “The effect of digital imagestabilization on coding performance”, International Symposium on Intelligent Multimedia, Video and SpeechProcessing, October, 2004。

图像显著性检测总结

图像显著性检测总结这块的工作一直在跟，一直没有整理，看到别人整理的资料不错，先加入作者链接：/anshan1984/article/details/8657176点击打开链接1. 早期C. Koch与S. Ullman的研究工作.他们提出了非常有影响力的生物启发模型。

C. Koch and S. Ullman . Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4):219-227, 1985.C. Koch and T. Poggio. Predicting the Visual World: Silence is Golden. Nature Neuroscience, 2(1):9–10, 1999.C.Koch是加州理工大学Koch Lab的教授，后文的侯晓迪师从C. Koch进行博士研究。

2. 南加州大学iLab实验室Itti教授及其学生Siagian等的研究工作.见/publications/. 主页提供iLab Neuromorphic Vision C++ Toolkit。

Christian Siagian博士期间的主要工作是生物学启发的机器人视觉定位研究（Biologically Inspired Mobile Robot Vision Localization）.L. Itti, C. Koch, & E. Niebur .A model of saliency based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259, 1998.L. Itti and C. Koch. Computational Modelling of Visual Attention. Nature Reviews Neuroscience, 2(3):194–203, 2001.L. Itti, & P. Baldi . Bayesian surprise attracts human attention. Advances in Neural Information Processing Systems, 19:547-554, 2005.C. Siagian, L. Itti, Comparison of gist models in rapid scene categorization tasks, In: Proc. Vision Science Society Annual Meeting (VSS08), May 2008.3. Caltech 的J. Harel研究工作.Koch Lab的J. Harel在2006年提出基于图的视觉显著性检测. 有Matlab实现。

幅度相位误差纠正英语

幅度相位误差纠正英语Amplitude and Phase Error Correction.Amplitude and phase error correction are crucial processes in various fields of engineering and science, particularly in signal processing, telecommunications, and electronics. These errors can arise due to various factors such as noise, distortion, and equipment limitations. Correcting these errors is essential to ensure accurate signal transmission and reliable system performance.Amplitude error correction involves adjusting the amplitude of a signal to its original or desired value. Amplitude errors can occur due to attenuation, gain variations, and nonlinearities in the signal path. To correct these errors, various techniques can be employed, such as gain adjustment, equalization, and amplitude compensation. Gain adjustment involves adjusting the amplifier gain to compensate for attenuation losses. Equalization techniques are used to correct frequency-dependent amplitude distortion by adjusting the signal's amplitude at different frequencies. Amplitude compensation techniques involve adjusting the signal amplitude to compensate for nonlinearities in the system.Phase error correction, on the other hand, involves adjusting the phase of a signal to align it with its original or desired phase. Phase errors can occur due to propagation delays, phase shifts introduced by components, and phase noise. To correct these errors, various phase correction techniques can be applied, such as phase alignment, phase compensation, and phase locking. Phase alignment techniques involve adjusting the phase of the signal to align it with a reference signal. Phase compensation techniques involve introducing an opposite phase shift to cancel out the phase error introduced by the system. Phase locking techniques involve using a phase-locked loop (PLL) or a frequency-locked loop (FLL) to maintain a constant phase relationship between two signals.Amplitude and phase error correction techniques are widely used in various applications, such as radar systems,satellite communications, audio processing, and image processing. In radar systems, amplitude and phasecorrection techniques are essential for accurate target detection and ranging. In satellite communications, these techniques are crucial for reliable signal transmission over long distances. In audio processing, amplitude and phase correction can improve sound quality by reducing distortion and noise. In image processing, these techniques can enhance image clarity and resolution by correcting distortions introduced during image acquisition or transmission.In conclusion, amplitude and phase error correction are fundamental processes in signal processing and system design. Correcting these errors is essential to ensure accurate signal transmission and reliable system performance. Various techniques and algorithms have been developed to address amplitude and phase errors, and their application depends on the specific application and system requirements. As technology continues to evolve, so will the methods and techniques used for amplitude and phaseerror correction, leading to more efficient and accurate systems in the future.。

Distortion correction device of distortion correct

专利名称：Distortion correction device of distortion correction method and the captured imagesignal of the captured image signal发明人：倉田徹,久保学,和田仁孝,皆見利行申请号：JP2004380683申请日：20041228公开号：JP4390068B2公开日：20091224专利内容由知识产权出版社提供摘要：A method and an apparatus for correcting distortions of image-taking video signals are provided. The method and an apparatus adopting the method are capable of reducing distortion generated in a taken image by correction of inter-frame and intra-frame hand movements in an image-taking apparatus employing an image-taking device of an X-Y address type. To be more specific, this method is capable of correcting distortion observed in a taken image as distortion caused by a positional change, which occurs in the horizontal and/or vertical directions of the taken image at a photographing time as a positional change of the image-taking device. In accordance with the method, in a frame period of a taken image, an inter-frame correction quantity for a positional change of the device is detected and, on the basis of the detected inter-frame correction quantity, the positional change is corrected to eliminate a displacement observed when the frame changes to the next one as a displacement of the taken image. An intra-frame correction quantity for a positional change of the image-taking device is computed for each of many locations on the screen of the taken image and, on the basis of the computed intra-frame correction quantity, distortions caused by positional changes onthe screen of the taken image as distortions of the taken image are corrected.申请人：ソニー株式会社地址：東京都港区港南１丁目７番１号国籍：JP代理人：佐藤正美更多信息请下载全文后查看。

Adaptive pixel defect correction

describe a Landweber-type iterative method for defect correction for grey-scale image data. Smith 4 estimates defect values by averaging a two-dimensional region of surrounding known pixels including the defective pixel. However, he notes that small intensity defects are not suﬃciently corrected with this method, and there is an apparent smearing eﬀect. Finally, Rashkovskiy and Macy5 interpolate the luminance image estimated by green Bayer pixels and luminance/chrominance diﬀerences in the Bayer pattern. However, the cubic B-spline ﬁlters used to interpolate the defects is computationally expensive to implement.
Further author information: (Send correspondence to primary author A.A.T.) E-mail: A.A.T.: anthony@, A.S.: arjen.van.der.sijde@, B.D.: bart.dillen@, A.T.: albert.theuwissen@, W.H.: wim.de.haan@philiபைடு நூலகம்

离轴三反时间延迟积分CCD相机内方位元素和畸变的标定

离轴三反时间延迟积分CCD相机内方位元素和畸变的标定吴国栋【摘要】由于测绘相机的关键几何参数内方位元素和畸变的标定精度决定相机的立体测绘精度,本文提出了一种离轴三反时间延迟积分( TDI) CCD相机内方位元素和畸变的标定方法.介绍了离轴三反TDICCD相机的光学系统和像面拼接方法,明确了该相机内方位元素和畸变的含义.建立了标定系统及相应的数学模型,应用最小二乘回归法求得了内方位元素和畸变的表达式.利用提出的方法标定了相机的内方位元素和畸变,并对标定误差进行了分析.结果表明:该方法对主点的标定精度可达1.0 μm(1σ),对主距的标定精度可达2.0 μm(1σ),对畸变的标定精度为2.3 μm(1σ).结果显示提出的标定方法快捷且有效.%The stereo mapping precision of mapping cameras depends on the calibration precision of the geometric parameters, such as inner orientation parameters and distortion, therefore, a new calibration method of inner orientation parameters and distortion for a three-mirror off-axis Time Delay Integration(TDD CCD camera was proposed. The optical system and image plane stitching of the camera were introduced, and the meanings of the inner orientation parameters and distortion were defined. A calibration system and corresponding mathematical model were established, then the expressions of inner orientation parameters and distortion were settled by the least square poly-regress method. A calibration experiment was performed on the camera, and results show that the point calibration accuracy and the focal calibration accuracy are better than 1. 0 μm(1σ) and 2. 0 μm(1σ) , respectively. Moreover, the distortion calibration accuracy has been 2. 3(um(la). Obtained results demonstrate that the calibration method has the advantages in the speed and efficiency for three-mirror off-axis TDICCD cameras.【期刊名称】《光学精密工程》【年(卷),期】2012(020)003【总页数】6页(P462-467)【关键词】时间延迟积分CCD相机;离轴三反相机;内方位元素;畸变;标定【作者】吴国栋【作者单位】中国科学院长春光学精密机械与物理研究所,吉林长春130033【正文语种】中文【中图分类】V447.3;TH7031 引言摄影测量是19世纪在测绘领域发展起来的一个新分支，其主要内容是利用摄影相机获取像的信息来测定空间点的位置。

L30000UNL L30002UNL 投影机快速参考卡说明书

Changing the Projection ModeYou can change the projection mode to flip the image over top-to-bottom or left-to-right using the projector menus.1. P ress the Menu button.2. S elect Extended > Projection .3. Select a projection mode.4. W hen you’re done, press the Menubutton to exit. You can also flip the image over top-to-bottom by pressing the Shutter button on the remote control for about five seconds.Note: To rotate the menu display, change the OSD Rotation setting in the Extended > Display menu.Switching Between Image SourcesPress the Search button (remote control) or one of the source buttons (remote control or projector).Changing the Screen Type SettingSet the Screen Type setting to the screen’s aspect ratio. 1. P ress the Menu button.2. S elect Extended > Display >Screen > Screen Type .Turning the Projector On1. T urn on your computer or imagesource.2. R emove the lens cover.3. P ress the On button. After thestatus light stays blue, the projector is ready.Note: If the Direct Power On function is enabled, you can turn the projector onwithout pressing the On button; just plug it in or flip the switch controlling the outlet to which the projector is connected. To turn on this feature, see the online User’s Guide .If the projector does not turn on, make sure the power switch on the side of the projector is turned on.If your image does not appear, see“Switching Between Image Sources” or “Troubleshooting.”Note: This projector requires a 220 Vpower source for normal operation. Limited functionality is temporarily available when connected to a 120 V power source. For details, see your online User Guide .Changing the Language of the Projector MenusThe default language of the projector’s menu system is English, but you can change it as necessary.1. P ress the Menu button to acess theprojector’s menu system.2. S elect Extended > Language .3. S elect your language.4. W hen you’re done, press the Menubutton to exit.2. P ress the or arrow buttons tomake the adjustment. 3. P ress the Esc button to finish theadjustment.Correcting Image ShapeIf your image is uneven on the sides, you can use the projector’s distortion correction features, such asH/V-Keystone and Quick Corner ®, to adjust the shape. See the online User’s Guide .Making Other Image AdjustmentsFor help on using the projector’sfeatures to improve the image quality, see the online User’s Guide . You can also view information on how to adjust the image color, position, and edges (Edge Blending) when projecting from multiple projectors to create one seamless widescreen image.Turning the Projector OffPress the Standby button to turn the projector off. If you see a confirmation message, press the Standby button again.Note: If the Direct Power On function isenabled, you can unplug the projector or flip the switch controlling the outlet to which the projector is connected instead. To turn on this feature, see the online User’s Guide .With Epson’s Instant Off ® technology, you don’t have to wait for the projector to cool down; just turn it off or unplug it when you’re done.3. S elect the screen’s aspect ratio.Note: The displayed image should matchthe screen’s size and shape.4. W hen you’re done, press the Menubutton to exit.Note: After changing the screen type, you may need to change the aspect ratio of the projected image depending on the input signal. Press the Aspect button on the remote control to change the aspect ratio, if necessary.Adjusting Image Position1. P ress the Lens Shift button on theremote control or projector.2. P ress the arrow buttons to adjustthe position of the projected image.3. W hen you’re done, press the Escbutton to finish the adjustment.Focusing and ZoomingPress the Focus or Zoom buttons on the remote control to adjust the image.You can also focus and zoom using the projector buttons.1. P ress the Focus/Distortion orZoom button on the projector.TroubleshootingIf you see a blank screen or the message No signal• M ake sure the status light on the projector is blue and not flashing,and the lens cover is removed.• M ake sure the cables are connected correctly. See the online User’sGuide.• Y ou may need to change the image source. See “Switching BetweenImage Sources.” Also make sure the source device is turned on.If the projector and the notebook don’t display the same image Windows®Press the function key on your keyboard that lets you display on an external monitor. It may be labeled CRT/LCD or have an icon such as . You may have to hold down the Fn key while pressing it (such as Fn + F7). Wait a moment for the display to appear. You may need to press the keys again to display the image on both devices.On Windows 7 or later, hold down the Windows key and press P at the same time, then click Duplicate.MacOpen System Preferences and select Displays. Click the Arrangementtab and select the Mirror Displays checkbox.Where to Get Help ManualsFor more information about using the projector, you can view or download the online manuals from the Epson website, as described below.Internet supportVisit /support (U.S.) or www.epson.ca/support (Canada) and search for your product to download software and utilities, view manuals, get FAQs and troubleshooting advice, or contact Epson.Speak to a support representativeTo use the Epson® PrivateLine® Support service, call (800) 637-7661. This service is available for the duration of your warranty period. You may also speak with a projector support specialist by dialing (562) 276-4394 (U.S.) or (905) 709-3839 (Canada).Support hours are 6 am to 8 pm, Pacific Time, Monday through Friday, and 7 am to 4 pm, Pacific Time, Saturday.Days and hours of support are subject to change without notice. Toll or long distance charges may apply. Purchase supplies and accessories You can purchase an air filter(V13H134A52), screens, and other accessories from an Epson authorized reseller. To find the nearest reseller,call 800-GO-EPSON (800-463-7766).Or you can purchase online at (U.S. sales) or www.epsonstore.ca (Canadian sales).Illuminate buttons temporarily Change the aspect ratioOpen menus assigned by userCorrect image distortionPort to connect remotecontrol cableControl one or multiple projectorsHold down and use numeric keysto select projector to control Hold down and use numeric keys to enter numbers Select color modes Turn the projector offSelect a sourceNavigate through menu settingsFreeze the image Open projector menusSelect menu settingsCancel current operation or return to previous menuAdjust the image positionSwitch image sourcesSave and apply presets Reset menu settings to their default valueDisplay the Info menuRemote Control MapTemporarily turn off displayDisplay a test patternShow or hide on-screen menusand messages Adjust keystonedistortionAdjust the image size Adjust the focusChanges the displayed test pattern; move to the next or previous image when projecting from a computer over the networkTurn the projector on Automatically adjustcomputer imageEPSON, Instant Off, and Quick Corner are registered trademarks and EPSON Exceed Your Vision is a registered logomark of Seiko Epson Corporation. PrivateLine is a registered trademark of Epson America, Inc.Windows is a registered trademark of Microsoft Corporation in the United States and/or other countries. Mac is a trademark of Apple Inc., registered in the U.S. and other countries.HDBaseT and the HDBaseT Alliance logo are trademarks of the HDBaseT Alliance.The Crestron Connected logo is a registered trademark of Crestron Electronics, Inc.HDMI and the HDMI Logo are trademarks or registered trademarks of HDMI Licensing Administrator, Inc.General Notice: Other product names used herein are for identification purposes only and may be trademarks of their respective owners. Epson disclaims any and all rights in those marks.This information is subject to change without notice.© 2020 Epson America, Inc., 6/20CPD-58962。