Fractional-order Sparse Representation for Image Denoising |
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Authors: | Leilei Geng Zexuan Ji Yunhao Yuan Yilong Yin |
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Affiliation: | 1.School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China2.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China3.School of Internet of Things Engineering, Jiangnan University, Wuxi 214112, China |
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Abstract: | 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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Keywords: | Fractional-order image denoising multi-scale sparse representation |
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