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Novel Image Deconvolution Algorithm Based on the ROF Model

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

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Abstract

The ROF (Rudin-Osher-Fatemi) model is one of the most successful and widely used models in image deconvolution. However, efficiently solving this model is usually impractical because of its non-smoothness. To solve the ROF model quickly and accurately to improve image deconvolution, a novel algorithm based on the splitting Bregman method and the two-step iterative thresholding method (2-SITM) is presented. The ROF model is decomposed into several sub-problems using the split Bregman method. These sub-problems are then solved by corresponding methods to obtain their closed-form solutions. To compute the denoising sub-problem, the 2-SITM is introduced. Compared with the popular iterative thresholding method, the 2-SITM is conducive to improving the performance of the presented algorithm. In an experiment, uniform-blurry images are restored by the 2-SITM to verify its effectiveness. Results also show the superior performance of the presented algorithm to some similar state-of-the-art algorithms.

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Acknowledgments

This work is supported by Anhui Provincial Natural Science Foundation (No. 1608085QF150).

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Authors and Affiliations

  1. School of Computer Science and Technology, Huaibei Normal University, Huaibei, 235000, China

    Su Xiao

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  1. Su Xiao
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Correspondence to Su Xiao .

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Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Xiao, S. (2016). Novel Image Deconvolution Algorithm Based on the ROF Model. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_53

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_53

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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