Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24804
Title: From heuristic optimization to dictionary learning : a review and comprehensive comparison of image denoising algorithms
Authors: Shao, L
Yan, R
Li, X
Liu, Y 
Keywords: Adaptive filters
Dictionary learning
Evaluation
Image denoising
Sparse coding
Spatial domain
Survey
Transform domain
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2014, v. 44, no. 7, 6587769, p. 1001-1013 How to cite?
Journal: IEEE transactions on cybernetics 
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.
URI: http://hdl.handle.net/10397/24804
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2013.2278548
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