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Journal of Electronic Imaging • new

Four-directional fractional-order total variation regularization for image denoising
Author(s): Linna Wu; Yingpin Chen; Jiaquan Jin; Hongwei Du; Bensheng Qiu
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Paper 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.

Paper Details

Date Published: 5 September 2017
PDF: 13 pages
J. Electron. Imag. 26(5) 053003 doi: 10.1117/1.JEI.26.5.053003
Published in: Journal of Electronic Imaging Volume 26, Issue 5
Show Author Affiliations
Linna Wu, Univ. of Science and Technology of China (China)
Yingpin Chen, Minnan Normal Univ. (China)
Univ. of Electronic Science and Technology of China (China)
Jiaquan Jin, Univ. of Science and Technology of China (China)
Hongwei Du, Univ. of Science and Technology of China (China)
Bensheng Qiu, Univ. of Science and Technology of China (China)

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