Image Restoration Using a Universal GMM Learning and Adaptive Wiener Filter

Nobumoto YAMANE  Motohiro TABUCHI  Yoshitaka MORIKAWA  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A   No.10   pp.2560-2571
Publication Date: 2009/10/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.2560
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Digital Signal Processing
Wiener filter,  Gaussian mixture distribution model,  optimal restoration filter,  adaptive filter,  statistical learning,  

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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.