Tools
ToolsShao, Ling, Yan, Ruomei, Li, Xuelong and Liu, Yan (2014) From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms. IEEE Transactions on Cybernetics, 44 (7). pp. 1001-1013. ISSN 2168-2267
Full text not available from this repository.Abstract
Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 02 Feb 2017 03:49 |
| Last Modified: | 03 Jul 2023 09:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/62273 |
| DOI: | 10.1109/TCYB.2013.2278548 |
Actions (login required)
 |
View Item |