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Article Dans Une Revue IEEE Transactions on Image Processing Année : 2019

Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a 100x Speed-Up

Résumé

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.

Dates et versions

ujm-02116234 , version 1 (30-04-2019)

Identifiants

Citer

Shibin Parameswaran, Charles-Alban Deledalle, Loïc Denis, Truong Nguyen. Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a 100x Speed-Up. IEEE Transactions on Image Processing, 2019, 28 (2), pp.687-698. ⟨10.1109/TIP.2018.2866691⟩. ⟨ujm-02116234⟩
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