应用残差总变分及低秩表示的视频去噪算法  

A Video Denoising Algorithm Using Residual Total Variation and Low-Rank Representation

作　　者：张智勇[1] 常侃[1,2,3] 陈诚[1] 施静兰 覃团发[1,2,3] 

机构地区：[1]广西大学计算机与电子信息学院,广西南宁530004 [2]广西大学广西多媒体通信与网络技术重点实验室培育基地,广西南宁530004 [3]广西大学广西高校多媒体通信与信息处理重点实验室,广西南宁530004

出　　处：《信号处理》2016年第5期558-566,共9页Journal of Signal Processing

基　　金：国家自然科学基金(61401108;61261023);广西自然科学基金(2013GXNSFBA019272)

摘　　要：为了有效地去除视频当中的高斯噪声和脉冲噪声,提出了一种新的视频去噪算法。该算法通过相似图像块组内的残差值总变分及低秩表示来同时探索图像块内的局部相似性以及图像块之间的相似性。首先,采用块匹配的方式在含噪视频中寻找最相似图像块并组合成图像块组;其次将每个相似图像组表达为一个低秩矩阵及一个稀疏矩阵之和,并同时强调低秩矩阵内的残差总变分范数最小化;最后,通过求解最优化问题获得最终的低秩矩阵,即恢复出的图像块组数据。实验结果表明,本文的算法能够有效去除视频当中含有的高斯噪声和脉冲噪声。与同类算法相比,能够获得显著的峰值信噪比提升。In order to effectively remove the Gaussian noise and impulse noise from video sequences,a new video denoising algorithm is proposed. This algorithm simultaneously exploits the local and nonlocal similarity among image blocks in a video sequence by utilizing total variation（ TV） of the residual values and low-rank representation of groups of similar image blocks. First of all,block matching is applied in a noisy video sequence to find the most similar image blocks,after which similar image blocks are grouped together. Then,every group of similar image blocks is represented as the sum of a lowrank matrix and a sparse matrix. In addition,the TV minimization of residual values in the low-rank matrix is also required. Finally,the target optimization problem is efficiently solved so as to obtain the low-rank matrix,which is the final recovered group of image blocks. Experimental results show that the proposed algorithm can well remove both the Gaussian noise and the impulse noise. Compared with other algorithms,our method is able to achieve significantly higher peak signal-to-noise ratio（ PSNR）.

关 键 词：视频去噪算法 残差总变分范数 低秩表示 块匹配 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]