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.
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