Multiple Subspace Model and Image-Inpainting Algorithm Based on Multiple Matrix Rank Minimization

Tomohiro TAKAHASHI  Katsumi KONISHI  Kazunori URUMA  Toshihiro FURUKAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.12   pp.2682-2692
Publication Date: 2020/12/01
Publicized: 2020/08/31
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDP7086
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
matrix rank minimization,  image inpainting,  multiple linear models,  

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This paper proposes an image inpainting algorithm based on multiple linear models and matrix rank minimization. Several inpainting algorithms have been previously proposed based on the assumption that an image can be modeled using autoregressive (AR) models. However, these algorithms perform poorly when applied to natural photographs because they assume that an image is modeled by a position-invariant linear model with a fixed model order. In order to improve inpainting quality, this work introduces a multiple AR model and proposes an image inpainting algorithm based on multiple matrix rank minimization with sparse regularization. In doing so, a practical algorithm is provided based on the iterative partial matrix shrinkage algorithm, with numerical examples showing the effectiveness of the proposed algorithm.