关键词: 鲁棒主分量分析, 稀疏, 低秩, 混合噪声

Abstract:

To overcome the drawbacks of the existing image recovery method failing to separate the dense noise and sparse noise, a tri-decomposition model is proposed. This model decomposes the matrix into three component matrices. The first matrix represents clean data, assumed being low rank and measured by nuclear norm; second matrix represents noise data, assumed being sparse and measured by L1 norm; third matrix also represents noise data, but assumed being dense and measured by F norm. The observed data can be divided into clean data, sparse noise and dense noise by these three measurement functions. Furthermore, the algorithm for tri-decomposition model is designed, which is based on the multiplier alternate direction method and is suit for separable variable optimization. This algorithm is used to process image data corrupted by large sparse noise and small dense noise. Experimental results in face images and surveillance videos demonstrate the effectiveness of the proposed method.

Key words: robust principal component analysis, sparse, low-rank, mixed noise