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A fast blind image deblurring method using salience map and gradient cepstrum

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Abstract

The prior-based blind image deblurring methods have recently achieved good performance. However, many state-of-art algorithms are time-consuming since some nonlinear operators are involved. Presented in this paper is a fast blind image deblurring algorithm which uses the salience map and gradient cepstrum. The inspiration for this work comes from the fact that the extreme values of the salience map of the clear image are more sparse than those of the blurred one. By enforcing the \(L_{0}\) norm constraint to the terms involving salience map and incorporating them into the traditional deblurring framework, an effective optimization scheme is explored. Furthermore, gradient cepstrum is used to adjust the number of iterations in each scale and determine the size of the initial kernel. Experimental results illustrate that our algorithm outperforms the state-of-art deblurring algorithms in both benchmark datasets and real blur scenes. Besides, this algorithm greatly shortens the running time since it restrains excessive iterations and does not involve any nonlinear operators.

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Acknowledgements

We would like to thank the reviewers for their helpful comments and suggestions which greatly improve the quality of the paper. This work was supported by the National Natural Science Foundation of China under Grant 62172135.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, 230601, China

    Jing Liu

  2. School of Mathematics, Hefei University of Technology, Hefei, 230009, China

    Jieqing Tan & Lei He

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  1. Jing Liu
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  2. Jieqing Tan
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  3. Lei He
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Liu, J., Tan, J. & He, L. A fast blind image deblurring method using salience map and gradient cepstrum. Vis Comput 39, 3091–3107 (2023). https://doi.org/10.1007/s00371-022-02515-0

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