Compressive Learning for Patch-Based Image Denoising

The expected patch log-likelihood algorithm (EPLL) and its extensions have shown good perfor-mances for image denoising. The prior model used by EPLL is usually a Gaussian mixture model (GMM) estimated from a database of image patches. Classical mixture model estimation meth-ods face computational issues as the high dimensionality of the problem requires training on large datasets. In this work, we adapt a compressive statistical learning framework to carry out the GMM estimation. With this method, called sketching, we estimate models from a compressive representa-tion (the sketch) of the training patches. The cost of estimating the prior from the sketch no longer depends on the number of items in the original large database. To accelerate further the estimation, we add another dimension reduction technique (low-rank modeling of the covariance matrices) to the compressing learning framework. To demonstrate the advantages of our method, we test it on real large-scale data. We show that we can produce denoising performances similar to performances obtained with models estimated from the original training database using GMM priors learned from the sketch with improved execution times.