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Spectral Response Based Regularization Parameter Selection for Total Variation Image Restoration

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Advanced Multimedia and Ubiquitous Engineering (FutureTech 2017, MUE 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 448))

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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

  1. College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yuhui Zheng, Min Li & Kai Ma

  2. College of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Shunfeng Wang

  3. College of Information Engineering, Yangzhou University, Yangzhou, 215127, China

    Jin Wang

Authors
  1. Yuhui Zheng
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  2. Min Li
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  3. Kai Ma
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  4. Shunfeng Wang
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  5. Jin Wang
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Corresponding author

Correspondence to Yuhui Zheng .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. (Jong Hyuk) Park

  2. School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  3. 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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  • DOI: https://doi.org/10.1007/978-981-10-5041-1_101

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5040-4

  • Online ISBN: 978-981-10-5041-1

  • eBook Packages: EngineeringEngineering (R0)

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