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Proceedings Paper

A none-blind deblurring algorithm for noisy images via distributed gradient prior
Author(s): Jiazi Huang; Qi Li; Huajun Feng; Zhihai Xu; Yueting Chen
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Paper Abstract

This paper proposes a none-blind deblurring algorithm for noisy images via distributed gradient prior. The proposed image prior is motivated by observing the gradient properties of noisy images. Based on the prior of image noise's low gradient distribution, we propose an effective optimization method to deal with noisy and blurry images. In this paper, an image-gradient-related distributed factor is introduced to balance image deblurring and denoising. The distributed factor is related to image noise and works adaptively according to different noise levels of blurry images. Richardson-Lucy method is also adopted to achieve a better deconvolution result. Experiments show that our proposed method outperforms other deblurring algorithms in both preserving details and removing noise.

Paper Details

Date Published: 7 November 2018
PDF: 6 pages
Proc. SPIE 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 108320S (7 November 2018); doi: 10.1117/12.2507231
Show Author Affiliations
Jiazi Huang, Zhejiang Univ. (China)
Qi Li, Zhejiang Univ. (China)
Huajun Feng, Zhejiang Univ. (China)
Zhihai Xu, Zhejiang Univ. (China)
Yueting Chen, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 10832:
Fifth Conference on Frontiers in Optical Imaging Technology and Applications
Junhao Chu; Wenqing Liu; Huilin Jiang, Editor(s)

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