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Blind image deblurring based on adaptive redescending potential function and local patch fidelity term

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Abstract

Blind image deblurring is a fundamental and important task in the field of computer vision. With the continuous progress of technology, blind image deblurring methods have achieved remarkable development and wide application. In early research, methods based on the \(L_0+X\) paradigm have successfully solved the blind image deblurring problem to some extent. Recently, a new method based on the redescending potential function has also come into prominence. This new method is formally more concise, but the general redescending potential function does not adapt to different regional features of the image. To address this issue, we introduce an adaptive redescending potential function. This function adapts to different structural features based on the magnitude of the total curvature in different regions of the image. Additionally, we introduce a local block fidelity term to consider the difference in gradient information, which is not involved in previous blind deblurring methods. The efficacy of the proposed method is demonstrated through experimental results on benchmark datasets and real blurred images. Our method achieved improvements in PSNR results by 0.28 dB and 0.79 dB on the Köhler and Levin datasets, respectively. Similarly, on the Lai dataset, our method enhanced the PSNR by 0.77 dB and the SSIM by 0.0374. Moreover, the self-supervised model induced by our proposed method achieved a PSNR result on the Levin dataset that is 0.47 dB higher than other advanced self-supervised models. These results indicate that the method is effective and produces notable results.

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Data Availibility Statement

No datasets were generated or analysed during the current study.

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Acknowledgements

The research is supported by the Natural Science Foundation of Inner Mongolia Autonomous Region (2024MS01002) and the network information center of Inner Mongolia University.

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Authors and Affiliations

  1. School of Mathematical Sciences, Inner Mongolia University, No: 235 West College Road, Hohhot, 010000, Inner Mongolia, China

    Lulu Zhang, Qiyu Jin, Guoliang Zhao & Caiying Wu

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  1. Lulu Zhang
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  2. Qiyu Jin
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Lulu Zhang wrote the main content of the paper, and Professors Qiyu Jin, Guoliang Zhao, and Caiying Wu revised the paper.

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Correspondence to Caiying Wu.

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Zhang, L., Jin, Q., Zhao, G. et al. Blind image deblurring based on adaptive redescending potential function and local patch fidelity term. SIViP 18, 8847–8857 (2024). https://doi.org/10.1007/s11760-024-03512-7

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