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Blind Image Deblurring Based on Local Rank

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Abstract

Conventional algorithms for blind image deblurring are often inaccurate at blur kernel estimation, and the recovery effect is far from perfect. To address this, we propose a single-image blind deblurring method based on local rank. For this, we first impose adaptive threshold segmentation on a conventional local rank transform, which is subsequently used to construct a novel model for blind image deblurring. Next, a half-quadratic splitting method is adopted to estimate the blur kernel and an intermediate latent image, in alternating iterations. Finally, the desired latent image is obtained by linear combination of the hyper-Laplacian model and the total-variation-l2 model, where the weights are calculated from the adaptive local ranks. Experimental results using public datasets show that the proposed approach can accurately estimate the blur kernel and effectively suppress ringing effects.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant no. 61573273. It is also supported by the State Key Laboratory of Rail Transit Engineering Informatization (FSDI) under Grant no.SKLKZ19-01.

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Li Zhu, Long Jin, Jihua Zhu, Zhongyu Li & Zhiqiang Tian

  2. Kyushu Institute of Technology, Fukuoka, Japan

    Huimin Lu

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  1. Li Zhu
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  2. Long Jin
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  3. Jihua Zhu
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  4. Zhongyu Li
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  5. Zhiqiang Tian
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  6. Huimin Lu
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Correspondence to Jihua Zhu.

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Zhu, L., Jin, L., Zhu, J. et al. Blind Image Deblurring Based on Local Rank. Mobile Netw Appl 25, 1446–1456 (2020). https://doi.org/10.1007/s11036-019-01375-8

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  • DOI: https://doi.org/10.1007/s11036-019-01375-8

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