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Proceedings Paper

Selecting good regions to deblur via relative total variation
Author(s): Lerenhan Li; Hao Yan; Zhihua Fan; Hanqing Zheng; Changxin Gao; Nong Sang
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Paper Abstract

Image deblurring is to estimate the blur kernel and to restore the latent image. It is usually divided into two stage, including kernel estimation and image restoration. In kernel estimation, selecting a good region that contains structure information is helpful to the accuracy of estimated kernel. Good region to deblur is usually expert-chosen or in a trial-anderror way. In this paper, we apply a metric named relative total variation (RTV) to discriminate the structure regions from smooth and texture. Given a blurry image, we first calculate the RTV of each pixel to determine whether it is the pixel in structure region, after which, we sample the image in an overlapping way. At last, the sampled region that contains the most structure pixels is the best region to deblur. Both qualitative and quantitative experiments show that our proposed method can help to estimate the kernel accurately.

Paper Details

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090L (8 March 2018);
Show Author Affiliations
Lerenhan Li, Huazhong Univ. of Science and Technology (China)
Hao Yan, Huazhong Univ. of Science and Technology (China)
Zhihua Fan, Shanghai Institute of Space Control Technology (China)
Hanqing Zheng, Shanghai Institute of Space Control Technology (China)
Changxin Gao, Huazhong Univ. of Science and Technology (China)
Nong Sang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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