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• W2080424460 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." @default.
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• W2080424460 date "2009-01-01" @default.
• W2080424460 modified "2023-09-27" @default.
• W2080424460 title "Image Restoration Using a Universal GMM Learning and Adaptive Wiener Filter" @default.
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• W2080424460 doi "https://doi.org/10.1587/transfun.e92.a.2560" @default.
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