A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration


Ganghua Liu, Wei Tian, Yushun Luo, Juncheng Zou, Shu Tang, Journal of Information Processing Systems Vol. 18, No. 1, pp. 48-58, Feb. 2022  

https://doi.org/10.3745/JIPS.04.0233
Keywords: Edge Amplitude, Image restoration, Kernel, Spatial Scale, Windowed-Total-Variation
Fulltext:

Abstract

Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.


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Cite this article
[APA Style]
Liu, G., Tian, W., Luo, Y., Zou, J., & Tang, S. (2022). A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration. Journal of Information Processing Systems, 18(1), 48-58. DOI: 10.3745/JIPS.04.0233.

[IEEE Style]
G. Liu, W. Tian, Y. Luo, J. Zou, S. Tang, "A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration," Journal of Information Processing Systems, vol. 18, no. 1, pp. 48-58, 2022. DOI: 10.3745/JIPS.04.0233.

[ACM Style]
Ganghua Liu, Wei Tian, Yushun Luo, Juncheng Zou, and Shu Tang. 2022. A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration. Journal of Information Processing Systems, 18, 1, (2022), 48-58. DOI: 10.3745/JIPS.04.0233.