The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 Tikhonov와 BTV(양측 전체 변형) 정규화를 모두 활용하는 복원 알고리즘에 나타나는 문제를 다룹니다. 전자의 정규화는 사전 정보가 실제로 가장자리에서 실패하는 가우스 분포를 갖는다고 가정하는 반면, 후자의 정규화는 선택된 양측 필터의 매개변수에 크게 의존합니다. 이러한 문제를 극복하기 위해 우리는 지역적 적응형 정규화를 제안합니다. 제안된 알고리즘에서는 적응형 가중치를 갖는 일반 방향 정규화 함수를 사용합니다. 적응형 가중치는 부분적으로 복원된 영상의 속성을 기반으로 로컬 패치에서 추정됩니다. Tikhonov 정규화와 달리 적응형 가중치를 사용하여 가장자리 전체의 부드러움을 피할 수 있습니다. 또한 제안된 정규화 함수는 BTV 정규화와 달리 매개변수 선택에 의존하지 않습니다. 제안된 알고리즘에 대해 볼록성 조건과 수렴 조건이 도출됩니다.
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부
Osama AHMED OMER, Toshihisa TANAKA, "Image Restoration Based on Adaptive Directional Regularization" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 12, pp. 3344-3354, December 2009, doi: 10.1587/transfun.E92.A.3344.
Abstract: This paper addresses problems appearing in restoration algorithms based on utilizing both Tikhonov and bilateral total variation (BTV) regularization. The former regularization assumes that prior information has Gaussian distribution which indeed fails at edges, while the later regularization highly depends on the selected bilateral filter's parameters. To overcome these problems, we propose a locally adaptive regularization. In the proposed algorithm, we use general directional regularization functions with adaptive weights. The adaptive weights are estimated from local patches based on the property of the partially restored image. Unlike Tikhonov regularization, it can avoid smoothness across edges by using adaptive weights. In addition, unlike BTV regularization, the proposed regularization function doesn't depend on parameters' selection. The convexity conditions as well as the convergence conditions are derived for the proposed algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.3344/_p
부
@ARTICLE{e92-a_12_3344,
author={Osama AHMED OMER, Toshihisa TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Image Restoration Based on Adaptive Directional Regularization},
year={2009},
volume={E92-A},
number={12},
pages={3344-3354},
abstract={This paper addresses problems appearing in restoration algorithms based on utilizing both Tikhonov and bilateral total variation (BTV) regularization. The former regularization assumes that prior information has Gaussian distribution which indeed fails at edges, while the later regularization highly depends on the selected bilateral filter's parameters. To overcome these problems, we propose a locally adaptive regularization. In the proposed algorithm, we use general directional regularization functions with adaptive weights. The adaptive weights are estimated from local patches based on the property of the partially restored image. Unlike Tikhonov regularization, it can avoid smoothness across edges by using adaptive weights. In addition, unlike BTV regularization, the proposed regularization function doesn't depend on parameters' selection. The convexity conditions as well as the convergence conditions are derived for the proposed algorithm.},
keywords={},
doi={10.1587/transfun.E92.A.3344},
ISSN={1745-1337},
month={December},}
부
TY - JOUR
TI - Image Restoration Based on Adaptive Directional Regularization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3344
EP - 3354
AU - Osama AHMED OMER
AU - Toshihisa TANAKA
PY - 2009
DO - 10.1587/transfun.E92.A.3344
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2009
AB - This paper addresses problems appearing in restoration algorithms based on utilizing both Tikhonov and bilateral total variation (BTV) regularization. The former regularization assumes that prior information has Gaussian distribution which indeed fails at edges, while the later regularization highly depends on the selected bilateral filter's parameters. To overcome these problems, we propose a locally adaptive regularization. In the proposed algorithm, we use general directional regularization functions with adaptive weights. The adaptive weights are estimated from local patches based on the property of the partially restored image. Unlike Tikhonov regularization, it can avoid smoothness across edges by using adaptive weights. In addition, unlike BTV regularization, the proposed regularization function doesn't depend on parameters' selection. The convexity conditions as well as the convergence conditions are derived for the proposed algorithm.
ER -