Structured matrix rank minimization approach to image inpainting

This paper proposes a structured matrix rank minimization approach to a novel image inpainting. We utilize the autoregressive (AR) model to describe the gray level of image, and formulate the image inpainting problem as the signal recovery problem by estimating the model order. This problem is described as the rank minimization problem, which is NP hard in general. To solve the problem approximately, this paper proposes an algorithm utilizing the null space based alternating optimization (NSAO) algorithm. Numerical examples show that the proposed algorithm recovers missing pixels well.