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Parameter Space Reduction Based L2-norm Noise Constrained Estimation for Image Restoration
Guan, Yu1; Gan, Min1,2; Chen, Guang Yong3
2020-11-13
Conference Name2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)
Source Publication2020 7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020
Pages189-194
Conference Date2020/11/13-2020/11/15
Conference PlaceGuangzhou, China
Abstract

In this paper, the image restoration is formulated as an L2-norm noise constrained estimation problem. Realizing that the constrained optimization problem is a separable nonlinear least squares problem, we eliminate a part of the parameters from the objective function using the idea of variable projection algorithms. Then, the gradient projection method is used to optimize the reduced parameter space problem. The numerical experiments on image restoration illustrate that the proposed methodology accelerates the convergence rate significantly.

KeywordGradient Projection (Gp) Image Restoration L2-norm Noise Constrained Estimation Variable Projection (Vp)
DOI10.1109/ICCSS52145.2020.9336851
URLView the original
Language英語English
Scopus ID2-s2.0-85100874836
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Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.The College of Mathematics and Computer Science, Fuzhou University, Fujian, China
2.The College of Computer Science and Technology, Qingdao University, Qingdao, China
3.The Faculty of Science and Technology, University of Macau, Macau, Macao
Recommended Citation
GB/T 7714
Guan, Yu,Gan, Min,Chen, Guang Yong. Parameter Space Reduction Based L2-norm Noise Constrained Estimation for Image Restoration[C], 2020, 189-194.
APA Guan, Yu., Gan, Min., & Chen, Guang Yong (2020). Parameter Space Reduction Based L2-norm Noise Constrained Estimation for Image Restoration. 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020, 189-194.
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