Journal
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Volume 5, Issue 2, Pages 555-563Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2017.7510412
Keywords
Fractional-order; image denoising; multi-scale; sparse representation
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Funding
- National Natural Science Foundation of China [61573219, 61402203, 61401209, 61701192, 61671274]
- Opening Fund of Shandong Provincial Key Laboratory of Network Based Intelligent Computing
- Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions
- Fostering Project of Dominant Discipline and Talent Team of SDUFE
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Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation (FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster. Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.
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