TitleSPOS: Deblur image by using sparsity prior and outlier suppression
AuthorsZhang, Yiwei
Li, Ge
Guo, Xiaoqiang
Wang, Wenmin
Wang, Ronggang
AffiliationSchool of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Lishui Road 2199, Nanshan District, Shenzhen, Guangdong Province, 518055, China
Academy of Broadcasting Science, SAPPRET, China Fuxingmen Outer Street 2, Xicheng District, Beijing, 100866, China
Issue Date2018
Publisher18th Pacific-Rim Conference on Multimedia, PCM 2017
Citation18th Pacific-Rim Conference on Multimedia, PCM 2017. 2018, 10735 LNCS, 138-148.
AbstractIn this paper, we propose an effective and robust method SPOS (Sparsity Prior and Outlier Suppression) for blurry image restoration. First, we combine histogram equalization and prior constrain to obtain the salient structure which contains main texture of image. Second, kernel is estimated by salient structure and sparsity constrain. Final, the blurry image is restored by non-blind deconvolution. The contributions of SPOS lie in two aspects: (1) we combine histogram equalization and sparsity suppression to obtain salient structure; (2) we take kernel outliers into consideration and introduce L0norm to suppress kernel’s shape. The experiment results show that SPOS has the better performance compared with the state-of-the-art methods. © Springer International Publishing AG, part of Springer Nature 2018.
Appears in Collections:信息工程学院

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