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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Luminance-Guided Chrominance Denoising with Debiased Coupled Total Variation
verfasst von : Fabien Pierre, Jean-François Aujol, Charles-Alban Deledalle, Nicolas Papadakis
Erschienen in: Energy Minimization Methods in Computer Vision and Pattern Recognition
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Abstract
This paper focuses on the denoising of chrominance channels of color images. We propose a variational framework involving TV regularization that modifies the chrominance channel while preserving the input luminance of the image. The main issue of such a problem is to ensure that the denoised chrominance together with the original luminance belong to the RGB space after color format conversion. Standard methods of the literature simply truncate the converted RGB values, which lead to a change of hue in the denoised image. In order to tackle this issue, a “RGB compatible” chrominance range is defined on each pixel with respect to the input luminance. An algorithm to compute the orthogonal projection onto such a set is then introduced. Next, we propose to extend the CLEAR debiasing technique to avoid the loss of colourfulness produced by TV regularization. The benefits of our approach with respect to state-of-the-art methods are illustrated on several experiments.