关键词: 鲁棒主成分分析, 整体关联性, 非局部关联性, 低秩, 视频去噪, 脉冲噪声

Abstract: Videos possess global correlation and non-local correlation based on image patches. Concerning the problem that existing video restoration methods only utilize one of these correlation properties，which limits the performance of video restoration algorithms，by considering these two low rank properties，a new video restoration algorithm based on global correlation and non-local correlation of video data was proposed. Firstly，the long-term global correlation of video frames was used to decompose the video corrupted by noise into global low-rank components and sparse residuals. Secondly，for residual part，there is non-local correlation between adjacent frames，non-local technique based on k-dimensional tree was utilized to form a group of similar patches，then low-rank decomposition model was used to process a group of image patches so that noise can be removed to obtain a clean image patch structure. Finally，the global low-rank part was added to the processed residual part to obtain a clean image. In the experiment of removing impulse noise from the noisy videos，the average Peak Signal-to-Noise Ratio（PSNR）of the proposed algorithm is 1. 3 dB higher than that of joint low rank and sparse aprroximation algorithm and 2 dB higher than that of robust temporal-spatial decomposition method. The experimental results show that the proposed algorithm is effective and superior.

Key words: Robust Principal Component Analysis (RPCA), global correlation, non-local correlation, low-rank, video denoising, impulse noise