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 위너 필터를 이용한 영상 복원 방법을 제안한다. 제안하는 방법은 공간적으로 변화하는 통계를 갖는 영상과 위너 필터의 이론을 일치시키기 위해 이전에 잡음 제거를 위해 제안된 UNI-GMM(Universal Gaussian Mixture Distribution Model)을 기반으로 하는 국지적 적응형 Wiener 필터(AWF)를 채택합니다. 제안하는 방법은 디콘볼루션 문제에 UNI-GMM-AWF를 적용하여 전치 필터로 SWF(Stationary Wiener Filter)를 사용한다. 이산 코사인 변환 영역의 SWF는 흐림 점 확산 기능을 축소하고 진행되는 AWF에서 모델링 및 필터링을 용이하게 합니다. SWF와 UNI-GMM은 일반 훈련 이미지 세트를 사용하여 학습되고 제안된 방법은 이미지 세트에 맞게 조정됩니다. 제안된 방법의 효율성을 입증하기 위해 시뮬레이션 결과를 제시한다.
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Nobumoto YAMANE, Motohiro TABUCHI, Yoshitaka MORIKAWA, "Image Restoration Using a Universal GMM Learning and Adaptive Wiener Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 10, pp. 2560-2571, October 2009, doi: 10.1587/transfun.E92.A.2560.
Abstract: In this paper, an image restoration method using the Wiener filter is proposed. In order to bring the theory of the Wiener filter consistent with images that have spatially varying statistics, the proposed method adopts the locally adaptive Wiener filter (AWF) based on the universal Gaussian mixture distribution model (UNI-GMM) previously proposed for denoising. Applying the UNI-GMM-AWF for deconvolution problem, the proposed method employs the stationary Wiener filter (SWF) as a pre-filter. The SWF in the discrete cosine transform domain shrinks the blur point spread function and facilitates the modeling and filtering at the proceeding AWF. The SWF and UNI-GMM are learned using a generic training image set and the proposed method is tuned toward the image set. Simulation results are presented to demonstrate the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2560/_p
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@ARTICLE{e92-a_10_2560,
author={Nobumoto YAMANE, Motohiro TABUCHI, Yoshitaka MORIKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Image Restoration Using a Universal GMM Learning and Adaptive Wiener Filter},
year={2009},
volume={E92-A},
number={10},
pages={2560-2571},
abstract={In this paper, an image restoration method using the Wiener filter is proposed. In order to bring the theory of the Wiener filter consistent with images that have spatially varying statistics, the proposed method adopts the locally adaptive Wiener filter (AWF) based on the universal Gaussian mixture distribution model (UNI-GMM) previously proposed for denoising. Applying the UNI-GMM-AWF for deconvolution problem, the proposed method employs the stationary Wiener filter (SWF) as a pre-filter. The SWF in the discrete cosine transform domain shrinks the blur point spread function and facilitates the modeling and filtering at the proceeding AWF. The SWF and UNI-GMM are learned using a generic training image set and the proposed method is tuned toward the image set. Simulation results are presented to demonstrate the effectiveness of the proposed method.},
keywords={},
doi={10.1587/transfun.E92.A.2560},
ISSN={1745-1337},
month={October},}
부
TY - JOUR
TI - Image Restoration Using a Universal GMM Learning and Adaptive Wiener Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2560
EP - 2571
AU - Nobumoto YAMANE
AU - Motohiro TABUCHI
AU - Yoshitaka MORIKAWA
PY - 2009
DO - 10.1587/transfun.E92.A.2560
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2009
AB - In this paper, an image restoration method using the Wiener filter is proposed. In order to bring the theory of the Wiener filter consistent with images that have spatially varying statistics, the proposed method adopts the locally adaptive Wiener filter (AWF) based on the universal Gaussian mixture distribution model (UNI-GMM) previously proposed for denoising. Applying the UNI-GMM-AWF for deconvolution problem, the proposed method employs the stationary Wiener filter (SWF) as a pre-filter. The SWF in the discrete cosine transform domain shrinks the blur point spread function and facilitates the modeling and filtering at the proceeding AWF. The SWF and UNI-GMM are learned using a generic training image set and the proposed method is tuned toward the image set. Simulation results are presented to demonstrate the effectiveness of the proposed method.
ER -