Subspace clustering and multiple matrix rank minimization approach to image inpainting algorithm

This paper proposes an image inpainting algorithm based on generalized principal component analysis (GPCA). Several inpainting algorithms have been proposed based on the assumption that an image can be modeled by the autoregressive (AR) model. However, their performances are not good enough to apply to natural photographs because they assume that images are modeled by the position-invariant linear model. To improve the inpainting quality, this work introduces a multiple AR model approach to image inpainting based on the generalized principle component analysis and proposes a new multiple matrix rank minimization approach. A practical algorithm is provided based on the iterative partial matrix shrinkage (IPMS) algorithm, and numerical examples show that the effectiveness of the proposed algorithm.