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

Abstract : 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 512×512 pixel image in under 0.5s for all the degradations mentioned above without specialized code optimizations such as CPU parallelization or GPU implementation.
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https://hal.archives-ouvertes.fr/hal-01617722
Contributeur : Charles-Alban Deledalle <>
Soumis le : mardi 17 octobre 2017 - 01:52:30
Dernière modification le : mercredi 25 octobre 2017 - 01:15:21

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  • HAL Id : hal-01617722, version 1

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Shibin Parameswaran, Charles-Alban Deledalle, Loïc Denis, Truong Q. Nguyen. Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100× speed-up. 2017. 〈hal-01617722〉

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