Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response


Yuhui Zheng, Kai Ma, Qiqiong Yu, Jianwei Zhang, Jin Wang, Journal of Information Processing Systems Vol. 13, No. 5, pp. 1168-1182, Oct. 2017  

https://doi.org/10.3745/JIPS.02.0072
Keywords: Image denoising, Local Spectral Response, Regularization Parameter Selection
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Abstract

In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.


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Cite this article
[APA Style]
Zheng, Y., Ma, K., Yu, Q., Zhang, J., & Wang, J. (2017). Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response. Journal of Information Processing Systems, 13(5), 1168-1182. DOI: 10.3745/JIPS.02.0072.

[IEEE Style]
Y. Zheng, K. Ma, Q. Yu, J. Zhang, J. Wang, "Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1168-1182, 2017. DOI: 10.3745/JIPS.02.0072.

[ACM Style]
Yuhui Zheng, Kai Ma, Qiqiong Yu, Jianwei Zhang, and Jin Wang. 2017. Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response. Journal of Information Processing Systems, 13, 5, (2017), 1168-1182. DOI: 10.3745/JIPS.02.0072.