Image Recovery by Decomposition with Component-Wise Regularization

Shunsuke ONO; Takamichi MIYATA; Isao YAMADA; Katsunori YAMAOKA

doi:10.1587/transfun.E95.A.2470

Image Recovery by Decomposition with Component-Wise Regularization

Shunsuke ONO, Takamichi MIYATA, Isao YAMADA, Katsunori YAMAOKA

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Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E95-A No.12 pp.2470-2478

Publication Date: 2012/12/01

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Online ISSN: 1745-1337

DOI: 10.1587/transfun.E95.A.2470

Type of Manuscript: PAPER

Category: Image

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Shunsuke ONO, Takamichi MIYATA, Isao YAMADA, Katsunori YAMAOKA, "Image Recovery by Decomposition with Component-Wise Regularization" in IEICE TRANSACTIONS on Fundamentals, vol. E95-A, no. 12, pp. 2470-2478, December 2012, doi: 10.1587/transfun.E95.A.2470.
Abstract: Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.2470/_p

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@ARTICLE{e95-a_12_2470,
author={Shunsuke ONO, Takamichi MIYATA, Isao YAMADA, Katsunori YAMAOKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Image Recovery by Decomposition with Component-Wise Regularization},
year={2012},
volume={E95-A},
number={12},
pages={2470-2478},
abstract={Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.},
keywords={},
doi={10.1587/transfun.E95.A.2470},
ISSN={1745-1337},
month={December},}

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TY - JOUR
TI - Image Recovery by Decomposition with Component-Wise Regularization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2470
EP - 2478
AU - Shunsuke ONO
AU - Takamichi MIYATA
AU - Isao YAMADA
AU - Katsunori YAMAOKA
PY - 2012
DO - 10.1587/transfun.E95.A.2470
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2012
AB - Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.
ER -