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

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 low-rank 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).