Mixed noise removal via robust constrained sparse representation

Liu, Licheng, Chen, C. L. Philip, You, Xinge, Tang, Yuan Yan, Zhang, Yushu and Li, Shutao 2017, Mixed noise removal via robust constrained sparse representation, IEEE transactions on circuits and systems for video technology, pp. 1-13, doi: 10.1109/TCSVT.2017.2722232.

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Title Mixed noise removal via robust constrained sparse representation
Author(s) Liu, Licheng
Chen, C. L. Philip
You, Xinge
Tang, Yuan Yan
Zhang, YushuORCID iD for Zhang, Yushu orcid.org/0000-0001-8183-8435
Li, Shutao
Journal name IEEE transactions on circuits and systems for video technology
Start page 1
End page 13
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017-06-30
ISSN 1051-8215
Keyword(s) image denoising
robust sparse representation
constrained sparse coding
dictionary learning
nonlocal self-similarity
Summary IEEE In recent years, the sparse coding based techniques have been widely used for image denoising. However, most of the sparse coding based mixed noise reduction methods fail to take full advantage of the geometric structure of data samples. In other words, they neglect the common information shared by the similar patches in sparse coding. To address this concern, in this paper we propose a Robust Constrained Sparse Representation (RCSR) method to remove mixed noise. By using the center coefficient of similar patches as the guider which is approximated by the coefficient of query patch in sparse coding, the geometric structure of data can be well preserved. Moreover, different from most existing two-stage mixed noise reduction methods that use explicit detectors to restrain impulse noise, the proposed RCSR adaptively adjusts the contribution of each pixel in the loss function to eliminate the influences of outliers. Experiments on the reconstruction of synthetic data and the removal of mixed noise in real images demonstrate the effectiveness of our proposed method.
Notes In Press
Language eng
DOI 10.1109/TCSVT.2017.2722232
Field of Research 0906 Electrical And Electronic Engineering
0801 Artificial Intelligence And Image Processing
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30103799

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