Four-directional fractional-order total variation regularization for image denoising

Linna Wu; Yingpin Chen; Jiaquan Jin; Hongwei Du; Bensheng Qiu

doi:10.1117/1.JEI.26.5.053003

5 September 2017 Four-directional fractional-order total variation regularization for image denoising

Linna Wu, Yingpin Chen, Jiaquan Jin, Hongwei Du, Bensheng Qiu

Author Affiliations +

Funded by: Natural Science Foundation of China

Journal of Electronic Imaging, Vol. 26, Issue 5, 053003 (September 2017). https://doi.org/10.1117/1.JEI.26.5.053003

Abstract

Noise removal is a fundamental problem in image processing. Among many approaches, total variation (TV) has attracted great attention because of its advantage in preserving edges. However, it tends to exhibit some undesired staircase artifacts. Fractional-order TV (FTV) can overcome the drawback mentioned above, yet it does not take enough neighborhood information into account. An extension of FTV, four-directional FTV (FTV4) is put forward to explore more directional information of an image. We solve this FTV4 model by adopting the split Bregman algorithm and fast Fourier transform theory. An accelerated step is added in the algorithm to make it converge faster. To decrease the computation time, we introduce the convolution theory and calculate the matrix difference in the frequency domain instead of space domain. Experimental results show that the proposed image denoising model performs better than other state-of-the-art models in most cases.

Citation Download Citation

Linna Wu, Yingpin Chen, Jiaquan Jin, Hongwei Du, and Bensheng Qiu "Four-directional fractional-order total variation regularization for image denoising," Journal of Electronic Imaging 26(5), 053003 (5 September 2017). https://doi.org/10.1117/1.JEI.26.5.053003

Received: 14 March 2017; Accepted: 9 August 2017; Published: 5 September 2017

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