Image Restoration Using a Universal GMM Learning and Adaptive Wiener Filter
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.10 pp.2560-2571
- Publication Date
- 2009/10/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.2560
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Keyword
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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},}
Copy
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 -
English
