IEICE TRANSACTIONS on Information
Multiple Subspace Model and Image-Inpainting Algorithm Based on Multiple Matrix Rank Minimization
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Tomohiro TAKAHASHI
Tokai University
Katsumi KONISHI
Hosei University
Kazunori URUMA
Kougakuin University
Toshihiro FURUKAWA
Tokyo University of Science
Keyword
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Tomohiro TAKAHASHI, Katsumi KONISHI, Kazunori URUMA, Toshihiro FURUKAWA, "Multiple Subspace Model and Image-Inpainting Algorithm Based on Multiple Matrix Rank Minimization" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 12, pp. 2682-2692, December 2020, doi: 10.1587/transinf.2020EDP7086.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7086/_p
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@ARTICLE{e103-d_12_2682,
author={Tomohiro TAKAHASHI, Katsumi KONISHI, Kazunori URUMA, Toshihiro FURUKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Multiple Subspace Model and Image-Inpainting Algorithm Based on Multiple Matrix Rank Minimization},
year={2020},
volume={E103-D},
number={12},
pages={2682-2692},
abstract={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.},
keywords={},
doi={10.1587/transinf.2020EDP7086},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Multiple Subspace Model and Image-Inpainting Algorithm Based on Multiple Matrix Rank Minimization
T2 - IEICE TRANSACTIONS on Information
SP - 2682
EP - 2692
AU - Tomohiro TAKAHASHI
AU - Katsumi KONISHI
AU - Kazunori URUMA
AU - Toshihiro FURUKAWA
PY - 2020
DO - 10.1587/transinf.2020EDP7086
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2020
AB - 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.
ER -
English
