利用图像滤波算法实习对高椒盐噪声的去噪处理——杨建春

编号：____________

审定成绩：____________

毕业设计（论文）

设计（论文）题目:_利用图像滤波算法实现

对高椒盐噪声的去噪处理

单位（系别）：通信与信息工程系______

学生姓名：_______杨建春_________

专业：__电子信息工程________

班级：____06111203__________

学号：__10__________

指导教师：_____靳艳红___________

答辩组负责人：______________________

填表时间： 2016年5月

重庆邮电大学移通学院教务处制

重庆邮电大学移通学院毕业设计(论文)任务书设计(论文)题目利用图像滤波算法实现对高椒盐噪声的去噪处理

学生姓名杨建春系别通信与信息工程系专业电子信息工程

班级 06111203

指导教师靳艳红职称讲师联系电话

教师单位重庆邮电大学移通学院下任务日期2016年__1__月_ 4__日

摘要

图像是一种重要的信息源，通过图像处理可以帮助人们了解信息的内涵。图像信号在获取和传输过程中，不可避免地受到各种噪声的污染，从而导致图像质量退化，对图像的后续处理，如边缘检测、图像分割、特征提取、模式识别等产生严重的影响，因此图像去噪是图像预处理的一个非常重要的环节。数字图像噪声去除涉及光学系统、微电子技术、计算机科学、数学分析等领域，是一门综合性很强的边缘科学，如今其理论体系已十分完善，且其实践应用非常广泛，在医学、军事、艺术、农业等方面都有广泛且成熟的应用[1]。

本文首先介绍了图像去噪的研究背景和意义、图像滤波算法的发展概况及方法;然后介绍了图像噪声的分类和数学模型，并着重介绍了传统的图像去噪算法：均值滤波器、中值滤波器和自适应滤波器以及对应的去噪算法。对常用的几种阈值去噪方法进行了分析比较和仿真实现。最后结合理论分析和实验结果，讨论了一个完整去噪算法中影响去噪性能的各种因素。为实际的图像处理中，去噪算法的选择和改进提供了数据参考和依据。

【关键词】自适应滤波器均值滤波器直方图梯度椒盐噪声加权中值滤波高斯降噪

ABSTRACT

Image is an important source of information by image processing can help people understand the connotation of information. And obtaining an image signal in the transmission process, inevitably contaminated all kinds of noise, resulting in degradation of image quality, image subsequent processing, such as edge detection, image segmentation, feature extraction and pattern recognition have a serious impact, thus denoising is a very important part of image preprocessing. It relates to the field of digital image noise removing optical systems, microelectronics, computer science, mathematical analysis, is a highly comprehensive interdisciplinary science, now has its theoretical system is perfect, and the practice is widely used in medical, military respect, art, agriculture and others have a wide range of sophisticated applications.

This paper introduces the research background and significance, image filtering algorithm development situation and method for image denoising; then introduces the classification and mathematical model of image noise, and highlights the traditional image denoising algorithm: mean filter, median filter and adaptive filter and the corresponding de-noising algorithm. Several commonly used thresholding method were analyzed and compared and Simulation. Finally, the theoretical analysis and experimental results, a complete discussion of the various factors affecting denoising denoising performance. The actual image processing, de-noising algorithm selection and improve the delivery of reference data and evidence.

【Key words】Adaptive filter Mean filter Salt and pepper noise gradient histogram Weighted median filtering Gaussian noise