How to Improve Your Denoising Result Without Changing Your Denoising AlgorithmMS14

In this talk we will review some recent approaches for improving denoising results, in the particular case of photographic images. This includes denoising a transformed version of the image rather than processing the image data directly, ensuring the image follows the noise model assumed by the denoising algorithm, and optimizing the parameters of the denoising method according to visual appearance, not to image quality metrics.

This presentation is part of Minisymposium “MS14 - Denoising in Photography and Video (2 parts)
organized by: Stacey Levine (Duquesne University) , Marcelo Bertalmío (University Pompeu Fabra) .

Stacey Levine (Duquesne University)
Marcelo Bertalmío (University Pompeu Fabra)
Gabriela Ghimpeteanu (Universitat Pompeu Fabra)
Thomas Batard (Technische Universität Kaiserslautern)
image enhancement, image representation, partial differential equation models, patch based methods