Angular-based pre-processing for image denoising

Javier Vazquez-Corral, Marcelo Bertalmío

Abstract:

There is not a large research on how to use color information for improving results in image denoising. Currently,
most of the methods modify the color space from sRGB to an opponent-like one as better results are obtained, but out of
this conversion, color is mostly ignored in the image denoising pipelines. In this work, we propose a color decomposition to
pre-process an image before applying a typical denoising. Our decomposition consists in obtaining a set of images in the
spherical coordinate system, each of them with the origin of the spherical transformation in a different color value. These color values, that we call color centers, are defined so as to be far away from the dominant colors of the image. Once in the spherical coordinate system, we perform a mild denoising operation with some state-of-the-art method in the angular components. Then, we convert these images back to sRGB, and we merge them depending on the distance between the color of each pixel and the color centers. Finally, we denoise the pre-processed image with the same state-of-the-art method used in our pre-processing. Experiments show that our method outperforms the results of directly applying the denoising method on the input image for different state-of-the-art denoising methods.