1. Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a $100{\times}$ Speed-Up.
- Author
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Parameswaran, Shibin, Deledalle, Charles-Alban, Denis, Loic, and Nguyen, Truong Q.
- Subjects
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IMAGE reconstruction , *IMAGE processing , *GAUSSIAN mixture models , *IMAGE quality analysis , *SIGNAL denoising - 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 the 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, the FEPLL is the first algorithm that can competitively restore a $512\times 512$ pixel image in under 0.5 s for all the degradations mentioned earlier without specialized code optimizations, such as CPU parallelization or GPU implementation. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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