[IPOL announce] new article: EPLL: An Image Denoising Method Using a Gaussian Mixture Model Learned on a Large Set of Patches

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2018/242/

Samuel Hurault, Thibaud Ehret, and Pablo Arias,
EPLL: An Image Denoising Method Using a Gaussian Mixture Model Learned 
on a Large Set of Patches,
Image Processing On Line, 8 (2018), pp. 465–489.
https://doi.org/10.5201/ipol.2018.242

Abstract
The Expected Patch Log-Likelihood method, introduced by Zoran and Weiss, 
allows for whole image restoration using a patch-based prior (in the 
likelihood sense) for which a maximum a-posteriori (MAP) estimate can be 
calculated. The prior used is a Gaussian mixture model whose parameters 
are learned from a dataset of natural images. This article presents a 
detailed implementation of the algorithm in the context of denoising of 
images contaminated with white additive Gaussian noise. In addition, two 
possible extensions of the algorithm to handle color images are compared.