IMAGE RESTORATION VIA EFFICIENT GAUSSIAN MIXTURE MODEL LEARNING

Expected Patch Log Likelihood (EPLL) framework using Gaussian Mixture Model (GMM) prior for image restoration was recently proposed with its performance comparable to the state-of-the-art algorithms. However, EPLL uses generic prior trained from offline image patches, which may not correctly represent statistics of the current image patches. In this paper, we extend the EPLL framework to an adaptive one, named A-EPLL, which not only concerns the likelihood of restored patches, but also trains the GMM to fit for the degraded image. To efficiently estimate GMM parameters in A-EPLL framework, we improve a recent Expectation-Maximization (EM) algorithm by exploiting specific structures of GMM from image patches, like Gaussian Scale Models. Experiment results show that A-EPLL outperforms the original EPLL significantly on several image restoration problems, like inpainting, denoising and deblurring.