【作者】 姜玉亭；

【导师】 尹忠科；

【作者基本信息】 西南交通大学 ， 信号与信息处理， 2005， 硕士

【摘要】 图像去噪是图像处理中的关键问题之一,是图像后续处理的基础。人们根据噪声特性,已经发展了多种图像去噪方法,它们在各自的适应范围内具有良好的去噪效果。但是这些方法往往依赖于图像信息以及图像噪声的统计特性,而在实际应用中,往往无法先验地获知图像和噪声的统计特性,从而无法获得好的去噪效果。 为了找到一种自适应的图像去噪方法,本文展开研究。基于图像稀疏分解和图像与噪声在稀疏分解中的不同表现,本文提出了一种自适应的图像去噪方法。具体在以下几个方面取得了一些进展: 1.首先针对传统的图像平滑去噪方法——均值滤波法的不足进行了改进,提出了形成不同模板的方法,从而针对不同的噪声水平可选取不同的模板,以达到最佳的平滑去噪效果,为后面提出的基于稀疏分解的图像去噪方法提供了可以比较的基础。 2.根据匹配跟踪图像稀疏分解的特点,结合人眼的视觉特性,选择较适合图像稀疏表达的非对称原子库。分析研究图像和噪声在稀疏分解中的不同表现,明确图像信息和噪声的区别。利用图像(或图像残余)和噪声与原子库相干性的不同,区分图像和噪声;以相干比阈值作为提取图像有用信息的结束条件,实现图像与噪声的自适应分离。实验证明了相干比阈值的选择与图像的类型和噪声水平没有关系,因而本文所提出的图像去噪方法是自适应的。 3.比较了稀疏分解图像去噪与最佳平滑模板去噪效果,就视觉效果来看,基于稀疏分解的图像去噪效果要好于最佳平滑模板的去噪效果。更多还原

【Abstract】 Image denoising is one of the key problems in image processing and the basis of image subsequent processing. Many image denoising methods have been developed based on the feature of noise, which have good performance in different conditions. However, these methods are always based on the statistic features of image signal and noise. Unfortunately, these features can not be known in advance in real applications.According to the different characters of image and noise in sparse decomposition, an adaptive image denoising method is proposed in this thesis. The research results are as following:As one traditional method for denoising, mean filtering has some short-comings. To overcome these drawbacks, one method is proposed to create different types of templates. For each noise level, only one kind of template is fit to obtain the optimal denoising result. The effect of this method is compared with that of the following method based on sparse decomposition.The anisotropic atom dictionary that is more suitable for image sparse representation is chosen according to the feature of the matching pursuit image sparse decomposition and human vision system. Based on study of the different behavior of image and noise in sparse decomposition, the difference between image and noise is identified. According to the different coherent ratio between image, noise and over-complete dictionary, image information and noise are distinguished. One image adaptive filtering is realized by taking coherent ratio threshold as the condition of ending the sparse decomposition process. Experimental results prove that the select of coherent ratio threshold is independent of image types or noise level. So the way of denoising presented here is adaptive.The effectness of image denoising between sparse decomposition and best smooth template are compared. According to the vision effect, image denoising results based on sparse decomposition is better than that of best smooth template.更多还原