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Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100x 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 512x512 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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Contributor : Charles-Alban Deledalle <>
Submitted on : Monday, August 27, 2018 - 2:06:32 PM
Last modification on : Wednesday, October 14, 2020 - 3:52:20 AM
Long-term archiving on: : Wednesday, November 28, 2018 - 12:32:38 PM


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  • HAL Id : hal-01617722, version 2
  • ARXIV : 1710.08124


Shibin Parameswaran, Charles-Alban Deledalle, Loïc Denis, Truong Q. Nguyen, Truong Nguyen. Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100x speed-up. 2017. ⟨hal-01617722v2⟩



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