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

• Published in: IEEE transactions on image processing Vol. 28; no. 2; pp. 687 - 698
• Main Authors: Parameswaran, Shibin, Deledalle, Charles-Alban, Denis, Loic, Nguyen, Truong Q
• Format: Journal Article
• Language: English
• Published: United States The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.02.2019
• Subjects: Algorithms; Image quality; Image restoration; Inverse problems; Noise reduction; Parallel processing; Probabilistic models
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2018.2866691

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 pixel image in under 0.5 s for all the degradations mentioned earlier without specialized code optimizations, such as CPU parallelization or GPU implementation.