TitleIMAGE DEBLURRING USING ROBUST SPARSITY PRIORS
AuthorsZhang, Xinxin
Wang, Ronggang
Tian, Yonghong
Wang, Wenmin
Gao, Wen
AffiliationPeking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Lishui Rd 2199, Shenzhen 518055, Peoples R China.
KeywordsMotion blur
deblurring
kernel estimation
image reconstruction
Issue Date2015
Publisher2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Citation2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP).Quebec City, CANADA,2015/1/1.
AbstractIn this paper, we propose a robust method to remove motion blur from a single photograph. We find that an inaccurate kernel and an unreliable final latent image reconstruction method are two main factors leading to low-quality restored images. To improve image quality, we do the following technical contributions. For robust blur kernel estimation, first, an edge mask and a smooth constraint are used to provide reliable intermediate latent images for salient structure extraction; second, we adopt an effective salient structure selection method to remove detrimental edges for kernel estimation; third, we use a gradient sparsity prior to remove kernel noise and ensure the continuity of blur kernels. For final latent image reconstruction, we combine the merits of both the TV-l(2) model and the hyper-Laplacian model to preserve tiny details and eliminate noise. Experimental results on synthetically blurred images and real photographs demonstrate that the proposed algorithm performs better than state-of-the-art approaches.
URIhttp://hdl.handle.net/20.500.11897/436434
ISSN1522-4880
DOI10.1109/ICIP.2015.7350775
IndexedEI
CPCI-S(ISTP)
Appears in Collections:信息工程学院

Files in This Work
There are no files associated with this item.

Web of Science®



Checked on Last Week

Scopus®



Checked on Current Time

百度学术™



Checked on Current Time

Google Scholar™





License: See PKU IR operational policies.