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Abstract
This letter introduces a total variation (TV) image restoration method with adaptive Regularization parameter. Our main contributions are two folds: (1) a novel selection scheme that determines the Regularization parameter of TV model in a global way through exploiting the concept of TV spectral response; (2) an efficient algorithm integrating an estimation-and–renewal strategy and the alternating minimization numerical technique to fast calculate the model solution. Experimental results on degraded images indicate the improved performance of our method, both in visual effects and in quantitative evaluations.
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Acknowledgments
This work was supported by the NSFC under Grants 61402235, the National Research Foundation of Korea under Grant 2011-001-7578, the Natural Science Foundation of Jiangsu Province under Grand BK20150923 and the PAPD.
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Authors and Affiliations
College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Yuhui Zheng, Min Li & Kai Ma
College of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Shunfeng Wang
College of Information Engineering, Yangzhou University, Yangzhou, 215127, China
Jin Wang
- Yuhui Zheng
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- Min Li
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- Kai Ma
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- Jin Wang
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Correspondence toYuhui Zheng.
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Editors and Affiliations
Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)
James J. (Jong Hyuk) Park
School of Computing and Information Sciences, Florida International University, Miami, Florida, USA
Shu-Ching Chen
Department of Information Systems and Cyber Security, The University of Texas at San Antonio, Adelaide, Australia
Kim-Kwang Raymond Choo
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Zheng, Y., Li, M., Ma, K., Wang, S., Wang, J. (2017). Spectral Response Based Regularization Parameter Selection for Total Variation Image Restoration. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_101
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