Abstract
In recent years, the performance of image denoising has been boosted drastically by nonlocal algorithms and sparse coding techniques. In this paper, we also take a nonlocal approach to image denoising and formulate the problem as one of collaborative LMMSE estimation from grouped image patches. We show that our optimal LMMSE solution amounts to shrinking the singular values of the matrix representation of the grouped image patches. This interpretation of our solution allows us to relate our estimation-theoretic approach to other nonlocal algorithms and sparse coding techniques in the literature. In addition, we develop an iterative algorithm to find the best LMMSE estimate. Experimental results show that our proposed denoising algorithm achieves better PSNR and subjective performance than the state of the art.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE International Conference Image Processing |
| Editors | Pascal Frossard, Marc Antonini |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 2714-2718 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479957514 |
| DOIs | |
| Publication status | Published - 28 Jan 2014 |
| Externally published | Yes |
| Event | IEEE International Conference on Image Processing 2014 - Paris, France Duration: 27 Oct 2014 → 30 Oct 2014 Conference number: 21st https://icip2014.wp.imt.fr/organizing-committee/ https://ieeexplore.ieee.org/xpl/conhome/6992914/proceeding (Proceedings) |
Conference
| Conference | IEEE International Conference on Image Processing 2014 |
|---|---|
| Abbreviated title | ICIP 2014 |
| Country/Territory | France |
| City | Paris |
| Period | 27/10/14 → 30/10/14 |
| Internet address |
Keywords
- Image denoising
- LMMSE estimation
- nonlocal algorithms
- sparse coding
- SVD