From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms

Shao, 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

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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: 22 Apr 2020 02:37
URI: https://ueaeprints.uea.ac.uk/id/eprint/62273
DOI: 10.1109/TCYB.2013.2278548

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