【作者】 杨涛；

【导师】 程文娟；

【作者基本信息】 合肥工业大学 ， 计算机应用技术， 2015， 硕士

【摘要】 在图像的获取、传输和存储过程中总是不可避免地引入各种噪声,因此对噪声的分析和处理是图像处理的经典问题。目前有很多性能不错的去噪算法,如非局部平均、BM3D等。但是这些算法一方面假设噪声图像的噪声水平为已知,实际上自然图像的噪声水平是未知的,并且噪声水平对去噪起到关键性的作用。另一方面寻找相似块非常耗时,很难应用到实际项目中。本文针对图像去噪这一经典难题进行研究,主要实现了图像噪声方差估计和基于噪声方差估计的自适应图像去噪方法,内容主要为以下两点：第一,图像噪声的估计算法：基于平滑块估计图像噪声的方法简单快速,但是在有噪声存在的情况下,仅根据方差很难准确地找到图像平滑块。针对这一问题,本文根据图像块的方差和信息熵,构造了新的选择平滑块的评价算子；并引入图像质量评价对候选图像噪声值进行进一步的迭代筛选；为了加快迭代速度利用基于最小堆的折半查找思想,在保证估计精度的同时尽量提高算法的处理速度。第二,改进的WNNM去噪算法：WNNM去噪算法对局部图像块迭代参数估计不准确以及块匹配耗时。针对以上问题,我们首先在图像噪声估计的基础上,提出了估计全局最优噪声方差作为WNNM去噪算法的初始参数；其次基于迭代过程中每个局部图像块的迭代参数仅根据自身的输入和输出结果计算,误差较大,提出了估计邻域最优参数代替局部参数；同时对算法中的块匹配策略使用两步搜索法进行改进,保证去噪结果,降低迭代次数。更多还原

【Abstract】 In the process of image acquisition, transmission and storage there are always all kinds of noises being introduced, so the analysis of noise and processing is a typical problem of image processing. There are many de-noising algorithms, whose performance is well, such as the non-local average and BM3D, etc. On the one hand, these algorithms, assume that the level of noise image known, while actually the noise level of natural images is unknown, and the noise level of de-noising plays a key role. On the other hand, it is time consuming to find similar blocks, so it is hard for us to apply it to actual projects.This thesis studies this typical problem for image de-noising, and has obtained a noise variance estimation algorithm and an adaptive image de-noising method which are based on the noise variance estimation. The main work of this thesis covers the following contributions:First, Estimation algorithm of image noise:Based on the smoothing block, the noise estimated method is simple and fast. However, in the presence of noise, it is difficult to accurately locate the image smoothing block according to the variance. To solve this problem, this paper, according to the variance and entropy of image blocks, constructed a new evaluation mode to select smooth block, and introduce the image quality evaluation for further iterative filtering to select a good results; Based on the minimum heap of binary search to speed up the iterative, it can ensures that the estimation precision at the same time improve the processing speed of the algorithm.Second, Improved WNNM de-noising algorithms:WNNM de-noising, which is based on the local image block, the iterative parameter estimation is not accurate and time-consuming about block-matching. Based on the proposed noise variance, we first estimated globally optimal de-noising algorithm as WNNM initial parameters; second iteration parameter of iterative process is based on each local image block and its own calculation of the input and output, so the error is large. The weighted nuclear norm minimization de-noising algorithms at each iteration of each partial image blocks are required to enter a noise variance parameters, so we used the estimated adjacent regions optimal parameters to replace the idea of local parameters to de-noising, and then proposed a block matching algorithm which is called two-step strategy acceleration tactics, we found it can reduce the number of iterations and ensure the de-noising result.更多还原