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Image restoration and reconstruction based on simple lenses computational imaging
Wang, X.-H.; J.-K. Hao; W. Huang and J.-H. Ouyang
2017
Source PublicationJilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
Volume47Issue:3
AbstractIn order to obtain high quality image information under the condition of compact optical lens structure, an approach for image restoration and reconstruction based on simple lenses computational imaging was proposed. First, by analyzing the characteristics of the Space Variant Point Spread Function (SVPSF) of the optical system, the method of SVPSF acquisition and measurement was presented. Then, the image sectioned restoration algorithm with SVPSF based adaptive sparse prior information was developed, which was used successfully in the restoration of blurry images through simple lenses. To validate the algorithm, the imaging results were obtained from through simple lenses imaging by the digital simulation and real images. Experimental results indicate that the algorithm can efficiently suppress the ringing effect and preserve the details of the images. 2017, Editorial Board of Jilin University. All right reserved.
Indexed Byei
Language中文
Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/59290
Collection中科院长春光机所知识产出
Recommended Citation
GB/T 7714
Wang, X.-H.,J.-K. Hao,W. Huang and J.-H. Ouyang. Image restoration and reconstruction based on simple lenses computational imaging[J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),2017,47(3).
APA Wang, X.-H.,J.-K. Hao,&W. Huang and J.-H. Ouyang.(2017).Image restoration and reconstruction based on simple lenses computational imaging.Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),47(3).
MLA Wang, X.-H.,et al."Image restoration and reconstruction based on simple lenses computational imaging".Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 47.3(2017).
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