深度学习下的图像视频处理技术

Quantitative Comparison: Our Dataset
Method HDRNet
DPE White-Box Distort-and-Recover Ours w/o ������������������������, w/o ������������������������,w/o ������������������������ Ours with ������������������������, w/o ������������������������, w/o ������������������������ Ours with ������������������������, with ������������������������, w/o ������������������������
Network Architecture
Advantage: Effective Learning and Efficient Learning
Ablation Study
Input
Naïve Regression
Expert-retouched
Our Dataset
Motivation:
The benchmark dataset is collected for enhancing general photos instead of underexposed photos, and contains a small number of underexposed images that cover limited lighting conditions.
Limitations of Previous Methods
Input
WVM [CVPR’16]
JieP [ICCV’17]
HDRNet [Siggraph’17]
DPE [CVPR’18]
White-Box [TOG’18]
Distort-and-Recover [CVPR’18]
Ours
Why This Model?
Our result
Expert-retouched
More Results
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
Our result
Expert-retouched
More Results
Input
JieP
HDRNet
DPE
White-box
Ours
PSNR 26.33 23.58 21.69 24.54 27.02 28.97 30.03 30.97
SSIM 0.743 0.737 0.718 0.712 0.762 0.783 0.822 0.856
Quantitative Comparison: MIT-Adobe FiveK
Method HDRNet
Input
Our result
More Results
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
Our result
Expert-retouched
More Results
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
技术创新，变革未来
深度学习下的图像视频处理技术
看得更清，看得更
懂
目录
1. 夜景增强 2. 图像视频去模糊 3. 视频超分辨率
1. 夜景图像增 强
Image Enhancement
Taking photos is easy
Photo Enhancement is required
Amateur photographers typically create underexposed photos
Ours
PSNR 28.61 24.66 23.69 28.41 28.81 29.41 30.71 30.80
SSIM 0.866 0.850 0.701 0.841 0.867 0.871 0.884 0.893
Visual Comparison: Our Dataset
Input
JieP
HDRNet
DPE White-Box Distort-and-Recover Ours w/o ������������������������, w/o ������������������������, w/o ������������������������ Ours with ������������������������, w/o ������������������������, w/o ������������������������ Ours with ������������������������, with ������������������������, w/o ������������������������
Existing Photo Editing Tools
Input
“Auto EnhaLeabharlann Baiduce” on iPhone
“Auto Tone” in Lightroom
Ours
Previous Work
Retinex-based Methods • LIME: [TIP 17] • WVM: [CVPR 16] • JieP: [ICCV 17] Learning-based Methods • HDRNet: [SIGGRAPH 17] • White-Box: [ACM TOG 18] • Distort-and-Recover: [CVPR 18] • DPE: [CVPR 18]
演示者 2019-05-08 03:51:55
-------------------------------------------The target of video super-solutionis to increase the resolution of videos with rich details. [click] It is an old and fundamental problemthat has been studied since several decades ago. [click] Video SR enables many applications,such as High-definition video generation from low-res sources. [click]
DPE
White-box
Distort-and-Recover
Our result
Expert-retouched
Visual Comparison: MIT-Adobe FiveK
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
Our result
Expert-retouched
Distort-and-Recover
Our result
Expert-retouched
More Results
Input
WVM
JieP
HDRNet
DPE
White-Box
Distort-and-Recover
Our result
More Results
Input
WVM
JieP
HDRNet
DPE
Motivation
Old and Fundamental Several decades ago [Huang et al, 1984] → near recent Many Applications HD video generation from low-res sources Video enhancement with details Text/object recognition in surveillance videos
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演示者 2019-05-08 03:51:55
-------------------------------------------[click] And also, it can benefit text or object recognition in low-quality surveillance videos. In this example, numbers on the car become recognizable only in the super-resolved result.
Image SR Traditional: [Freeman et al, 2002], [Glasner et al, 2009], [Yang et al, 2010], etc. CNN-based: SRCNN [Dong et al, 2014], VDSR [Kim et al, 2016], FSRCNN [Dong et al, 2016], etc.
Previous Work
演示者 2019-05-08 03:51:56
-------------------------------------------Previously, lots of work and methodsave been proposed in superresolution. [click] We list several representative methods here.
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演示者 2019-05-08 03:51:55
-------------------------------------------[click] Video enhancement with details. In this example, characters on the roof and textures of the tree in SR result are much clearer then input. [click]
White-Box
Distort-and-Recover
Our result
More Results
Input
iPhone
Lightroom
Our result
More Results
Input
iPhone
Lightroom
Our result
2. 视频超分辨 率
Motivation
Old and Fundamental Several decades ago [Huang et al, 1984] → near recent Many Applications HD video generation from low-res sources
More Comparison Results: User Study
Input
WVM
JieP
HDRNet
DPE
White-Box
Distort-and-Recover
Our result
Limitaion
演示者 2019-05-08 03:51:53
-------------------------------------------Our work also exists some limitations,the first limitation is the region is almost black without any trace of texture. We can see the top two images. The second limitation is our method doen’t clear noise in the enhanced result.
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Motivation
Old and Fundamental Several decades ago [Huang et al, 1984] → near recent Many Applications HD video generation from low-res sources Video enhancement with details
• Illumination maps for natural images typically have relatively simple forms with known priors.
• The model enables customizing the enhancement results by formulating constraints on the illumination.