| Paper: | MA-P3.7 |
| Session: | Image and Video Denoising |
| Time: | Monday, September 17, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
COLOR IMAGE DENOISING VIA SPARSE 3D COLLABORATIVE FILTERING WITH GROUPING CONSTRAINT IN LUMINANCE-CHROMINANCE SPACE |
| Authors: |
Kostadin Dabov; Tampere University of Technology | | |
| | Alessandro Foi; Tampere University of Technology | | |
| | Vladimir Katkovnik; Tampere University of Technology | | |
| | Karen Egiazarian; Tampere University of Technology | | |
| Abstract: |
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. |
