PARAMESWARAN, Shibin; DELEDALLE, Charles-Alban; DENIS, Loïc; NGUYEN, Truong Q.; NGUYEN, Truong

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PARAMESWARAN, Shibin
University of California [San Diego] [UC San Diego]

DELEDALLE, Charles-Alban
Institut de Mathématiques de Bordeaux [IMB]
University of California [San Diego] [UC San Diego]

DENIS, Loïc
Université Jean Monnet - Saint-Étienne [UJM]
Université de Lyon
Institut d'Optique Graduate School [IOGS]
Laboratoire Hubert Curien [LabHC]

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Document de travail - Pré-publication

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Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems such as denoising, deblurring, super-resolution, inpainting and devignetting. To the best of our knowledge, FEPLL is the first algorithm that can competitively restore a 512x512 pixel image in under 0.5s for all the degradations mentioned above without specialized code optimizations such as CPU parallelization or GPU implementation.< Réduire

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Index Terms—Image restoration

Image restoration

efficient algorithms

Gaussian mix- ture model

image patch

Gaussian mixture model

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Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100x speed-up

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Résumé en anglais

Mots clés en anglais

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