A Variational Model for Staircase Reduction in Image Denoising

TANG Liming; FANG Zhuang; XIANG Changcheng; CHEN Shiqiang; HUANG Darong

doi:10.1049/cje.2017.01.015

Volume 26 Issue 2

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TANG Liming, FANG Zhuang, XIANG Changcheng, et al., “A Variational Model for Staircase Reduction in Image Denoising,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 358-366, 2017, doi: 10.1049/cje.2017.01.015

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TANG Liming, FANG Zhuang, XIANG Changcheng, et al., “A Variational Model for Staircase Reduction in Image Denoising,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 358-366, 2017, doi: 10.1049/cje.2017.01.015

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A Variational Model for Staircase Reduction in Image Denoising

doi: 10.1049/cje.2017.01.015

1.
School of Science, Hubei University for Nationalities, Enshi 445000, China;
2.
School of information science and engineering, Chongqing Jiaotong University, Chongqing 400074, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61561019), the Natural Science Fund of Hubei Province (No.2015CFB262), the National Science and Technology Pillar Program (No.2015BAK27B03), and Doctoral Scientific Fund Project of Hubei University for Nationalities (No.MY2015B001).

Received Date: 2014-10-15
Rev Recd Date: 2015-04-06
Publish Date: 2017-03-10

Abstract

Abstract

We propose a new variational model to reduce the staircase that often appears in Total variation (TV) based models in image denoising. The model uses BV-seminorm and Besov-seminorm to measure the piecewise constant component and piecewise smooth component of the image, respectively. We discuss the nontrivial property of the proposed model and introduce an alternating iteration algorithm that combines the dual projection algorithm with Wavelet soft thresholding (WST) algorithm to solve the model numerically. The experimental results show that the proposed model is effective for noise removal and staircase reduction, while the contour can be preserved in the denoised images. Furthermore, compared with two classical staircase reduction models, CEP2 and TGV, the proposed model is much faster than these two models.
- Image denoising,
- Staircase,
- Variation,
- BV space,
- Besov space

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References(30)

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