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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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Contributor : Charles-Alban Deledalle Connect in order to contact the contributor
Submitted on : Tuesday, October 17, 2017 - 1:52:30 AM
Last modification on : Friday, December 3, 2021 - 12:20:05 PM
Long-term archiving on: : Thursday, January 18, 2018 - 12:37:43 PM


  • HAL Id : hal-01617722, version 1
  • ARXIV : 1710.08124


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-01617722v1⟩



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