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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Official URL: http://dx.doi.org/10.1109/TCYB.2013.2278548

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
Uncontrolled Keywords: Adaptive filters, dictionary learning, evaluation, image denoising, sparse coding, spatial domain, survey, transform domain
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
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Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 12:43
Last Modified: 03 Nov 2016 12:33
URI: http://nrl.northumbria.ac.uk/id/eprint/22820

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