EPLL: an image denoising method using a Gaussian mixture model learned on a large set of patches
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 …
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 …
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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