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A developed Criminisi algorithm based on particle swarm optimization (PSO-CA) for image inpainting

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Abstract

As a robust digital image inpainting technology, the Criminisi algorithm (CA) has been widely used. However, its high running time that it needs to search in the entire undamaged area of the image to determine an optimal matching block presents a challenge. To address this issue, this study proposes an improved version of CA, named PSO-CA, which incorporates the particle swarm optimization algorithm (PSO) with CA. The running time of the CA is significantly reduced benefiting from the parallel optimization capability of the PSO. In addition, the search space is restricted to the neighbouring region of the block that needs to be filled. The availability of the proposed PSO-CA algorithm is assessed in the laboratory colour model by the running time and three matching indices, such as the peak signal-to-noise ratio (PSNR). The experimental results indicate that PSO-CA significantly enhances the inpainting speed and produces the same or better results compared with the initial CA and the Criminisi with search space algorithm (CWSS).

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Data availability

The images used in this paper consist of two parts. Images used in Experiment 1 were taken pictures on our phone, and others used in Experiment 2 are openly available from https://github.com/cantarinigiorgio/Image-Inpainting/tree/master/images.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (U2340210) and the Project of Sanya Yazhou Bay Science and Technology City (SCKJ-JYRC-2022-100).

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Author notes

  1. Fang-Fang Li and Hui-Min Zuo have contributed equally to this study.

Authors and Affiliations

  1. College of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China

    Fang-Fang Li & Hui-Min Zuo

  2. College of Water Sciences, Beijing Normal University, Beijing, 100875, China

    Jun Qiu

  3. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China

    Ying-Hui Jia

  4. College of Water and Architectural Engineering, Shihezi University, Shihezi, 832003, China

    Fang-Fang Li

  5. Sanya Institute of China Agricultural University, China Agricultural University, Sanya, 572025, China

    Fang-Fang Li

  6. State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, 810016, China

    Jun Qiu

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  1. Fang-Fang Li
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  2. Hui-Min Zuo
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  3. Ying-Hui Jia
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  4. Jun Qiu
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Contributions

F-FL and H-MZ performed study concept and design, as well as development of methodology and writing, review and revision of the paper; Y-HJ and JQ provided acquisition, analysis and interpretation of data, and statistical analysis; F-FL and JQ provided financial support. All authors read and approved the final paper.

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Correspondence to Jun Qiu.

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The authors declare that they have no conflicts of interest to this work. The people involved in the experiment have been informed and formally accepted.

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Li, FF., Zuo, HM., Jia, YH. et al. A developed Criminisi algorithm based on particle swarm optimization (PSO-CA) for image inpainting. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06099-5

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  • DOI: https://doi.org/10.1007/s11227-024-06099-5

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