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Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(2): 555-563
Leilei Geng, Zexuan Ji, Yunhao Yuan and Yilong Yin, "Fractional-order Sparse Representation for Image Denoising," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 555-563, Mar. 2018. doi: 10.1109/JAS.2017.7510412
Citation: Leilei Geng, Zexuan Ji, Yunhao Yuan and Yilong Yin, "Fractional-order Sparse Representation for Image Denoising," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 555-563, Mar. 2018. doi: 10.1109/JAS.2017.7510412
shu

Fractional-order Sparse Representation for Image Denoising

doi: 10.1109/JAS.2017.7510412
  • 1.

    School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China

  • 2.

    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

  • 3.

    School of Internet of Things Engineering, Jiangnan University, Wuxi 214112, China

Funds:

the National Natural Science Foundation of China 61573219

the National Natural Science Foundation of China 61402203

the National Natural Science Foundation of China 61401209

the National Natural Science Foundation of China 61701192

the National Natural Science Foundation of China 61671274

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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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