关键词: 低秩稀疏分解, 鲁棒主成分分析, 视频前背景分离, 图像去噪, 鲁棒性

Abstract:

Low rank and sparse decomposition （LRSD） is a data representation technology and has been widely used in the field of computer vision and many other fields. The LRSD method is a mechanism which decomposes the known data matrix to the low rank and sparse components, and can be used to the practical applications such as video foreground and background separation, image denoising and so on. This paper gives these models, advantages and disadvantages of many LRSD methods based on the analysis of the current researches at home and abroad. Many methods are applied to the video foreground and background separation and image denoising. The experimental results of the video foreground and background separation include the extracted foreground objects, the F-measure values and the running time. The experimental results of image denoising include the denoising image, the PSNR values and the FSIM values. The results of the video foreground and background separation and image denoising show the advantages and disadvantages of these LRSD methods from the visual and quantitative perspectives.

Key words: low rank and sparse decomposition, robust principal component analysis, video foreground and background separation, image denoising, robustness