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Efficient Self-Adaptive Image Deblurring Based on Model Parameter Optimization
Yang, Haoyuan1,2; Su, Xiuqin1; Ju, Chunwu2; Wu, Shaobo2
2018
Conference Name3rd IEEE International Conference on Image, Vision and Computing (ICIVC)
Source Publication2018 IEEE 3RD INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC)
Pages384-388
Conference Date2018-06-27
Conference PlaceChongqing, PEOPLES R CHINA
PublisherIEEE
Contribution Rank1
Abstract

Natural images suffer from degradations in imaging system, and image blur is a major source of them. Most existing approaches aim to estimate a blur kernel via an alternating optimization method in multiscale space. However, in our practical project application, we need to deal with motion blurs come from moving conveyor belts. In this case, the degradation model and its orientation are known to us. In this paper, we propose a self-adaptive image deblurring method to deal with it. The model parameters are optimized by a heuristic algorithm, and the latent images are deblurred by a deconvolution technique based on l(1) -norm constraint. Simulation results show that our method not only acts on motion blur model, but also can deal with atmosphere turbulence model and defocus model, and the comparison results indicate that it outperforms others'. Furthermore, it is able to deal with motion blur in real scenes with high efficiency.

Department光电测量技术实验室
Indexed ByEI ; CPCI
ISBN978-1-5386-4991-6
Language英语
WOS IDWOS:000448170000074
EI Accession Number20184706085943
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30706
Collection光电测量技术实验室
Corresponding AuthorYang, Haoyuan
Affiliation1.Xian Inst Opt & Precis Mech, Xian 710119, Shanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Yang, Haoyuan,Su, Xiuqin,Ju, Chunwu,et al. Efficient Self-Adaptive Image Deblurring Based on Model Parameter Optimization[C]:IEEE,2018:384-388.
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