Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space

We propose an effective color image denoising method that exploits filtering in highly sparse local 3D transform domain in each channel of a luminance-chrominance color space. For each image block in each channel, a 3D array is formed by stacking together blocks similar to it, a process that we call...

Full description

Saved in:
Bibliographic Details
Published in2007 IEEE International Conference on Image Processing Vol. 1; pp. I - 313 - I - 316
Main Authors Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2007
Subjects
Online AccessGet full text
ISBN9781424414369
1424414369
ISSN1522-4880
DOI10.1109/ICIP.2007.4378954

Cover

More Information
Summary:We propose an effective color image denoising method that exploits filtering in highly sparse local 3D transform domain in each channel of a luminance-chrominance color space. For each image block in each channel, a 3D array is formed by stacking together blocks similar to it, a process that we call "grouping". The high similarity between grouped blocks in each 3D array enables a highly sparse representation of the true signal in a 3D transform domain and thus a subsequent shrinkage of the transform spectra results in effective noise attenuation. The peculiarity of the proposed method is the application of a "grouping constraint" on the chrominances by reusing exactly the same grouping as for the luminance. The results demonstrate the effectiveness of the proposed grouping constraint and show that the developed denoising algorithm achieves state-of-the-art performance in terms of both peak signal-to-noise ratio and visual quality.
ISBN:9781424414369
1424414369
ISSN:1522-4880
DOI:10.1109/ICIP.2007.4378954