We study an image denoising approach the core of which is a locally adaptive estimation of the probability that a given coefficient contains a significant noise-free component, which we call "signal of interest". We motivate this approach within the minimum mean squared error criterion and develop and analyze different locally adaptive versions of this method for color and for multispectral images in remote sensing. For color images, we study two different approaches: (i) using a joint spatial/spectral activity indicator in the RGB color space and (ii) componentwise spatially adaptive denoising in a luminance-chrominance space. We demonstrate and discuss the advantages of both of these approaches in different scenarios. We also compare the analyzed method to other recent wavelet domain denoisers for multiband data both on color and on multispectral images.

Date Published: 17 September 2005
PDF: 14 pages
Proc. SPIE 5914, Wavelets XI, 59141I (17 September 2005); doi: 10.1117/12.619386

Published in SPIE Proceedings Vol. 5914:
Wavelets XI
Manos Papadakis; Andrew F. Laine; Michael A. Unser, Editor(s)