Selecting good regions to deblur via relative total variation

Lerenhan Li; Hao Yan; Zhihua Fan; Hanqing Zheng; Changxin Gao; Nong Sang

doi:10.1117/12.2284374

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8 March 2018 Selecting good regions to deblur via relative total variation

Lerenhan Li, Hao Yan, Zhihua Fan, Hanqing Zheng, Changxin Gao, Nong Sang

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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106090L (2018) https://doi.org/10.1117/12.2284374
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

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Lerenhan Li, Hao Yan, Zhihua Fan, Hanqing Zheng, Changxin Gao, and Nong Sang "Selecting good regions to deblur via relative total variation", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090L (8 March 2018); https://doi.org/10.1117/12.2284374

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