Low-rank and nonlinear model approach to image inpainting

Abstract

This paper proposes a new algorithm for image inpainting algorithm based on the matrix rank minimization with nonlinear mapping function. Assuming that each intensity value of a nonlinear mapped image can be modeled by the autoregressive (AR) model, the image inpainting problem is formulated as a kind of the matrix rank minimization problem, and this paper modifies the iterative partial matrix shrinkage (IPMS) algorithm and provides an inpainting algorithm, which estimates a nonlinear mapping function and the missing pixels simultaneously. Numerical examples show that the proposed algorithm recovers missing pixels efficiently.