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A Super-Resolution Image Reconstruction Method for a Lensless Microscope

Published: 07 January 2025 Publication History

Abstract

The lensless microscopy technique, with the advantages of low cost and small size, can be widely applied in biotechnology and medicine. In this paper, an innovative image reconstruction scheme is proposed, which is based on the super resolution convolutional neural network (SRCNN). By optimizing the network structure and training strategy, high-quality image reconstruction is achieved for lensless microscopy images. To verify the method's effectiveness, we built a self-built dataset and trained and tested the neural network. The experimental results show that the proposed improved scheme achieves significantly better image reconstruction quality than the original SRCNN network, effectively reduces computational load, and improves reconstruction efficiency.

References

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Shuo Wen Li, Yun Hui Gao, Jia Chen Wu, Ming Jie Wang, Zhang Cheng Huang, Shumei Chen, Liangcai Cao, “Lensless camera: Unraveling the breakthroughs and prospects,”Fundamental Research, 2024.
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Aydogan Ozcan, Euan McLeod, “Lensless imaging and sensing,” Annual review of biomedical engineering, vol. 18, No. 1,pp. 77-102, 2016.
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Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, “Learning a deep convolutional network for image super-resolution,” European Conference on Computer Vision, pp. 184-199, 2014.
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Shao Wei Jiang, Zi Chao Bian, Jia Sai Zhu, Peng Ming Song, Guo, Cheng Fei Guo, He Zhang, Rui Hai Wang, Guoan Zheng, “High-throughput and field-portable ptychographic lensless on-chip microscopy based on translated pattern modulation,” High-Speed Biomedical Imaging and Spectroscopy V, Vol. 11250, p. 112500E.
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“Lensless on-chip microscopy based on diffraction analysis and deep learning,” Optics Express, vol. 28(11); pp. 16067-16079, 2020.
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H. Zhu, W. Luo, X. Tao, “Lensless imaging through deep learning,” Optica, vol. 6; pp. 5-6, 2019.
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J. D. Rego, K. Kulkarni, S. Jayasuriya, "Robust lensless image reconstruction via psf estimation.". In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 403-412, 2021.
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K. Hammernik, F. Knoll, “Machine learning for image reconstruction,” Handbook of medical image computing and computer assisted intervention. Academic Press, 2020.
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Suhail Hamdan, Yohei Fukumizu, Tomonori Izumi, and Hironori Yamauchi, "Face Image Super-Resolution with Adaptive Patch Size to Scaling Factor," Journal of Image and Graphics, Vol. 6, No. 2, pp. 167-173, December 2018.
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  1. A Super-Resolution Image Reconstruction Method for a Lensless Microscope

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    ICCPR '24: Proceedings of the 2024 13th International Conference on Computing and Pattern Recognition
    October 2024
    448 pages
    ISBN:9798400717482
    DOI:10.1145/3704323
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 07 January 2025

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    Author Tags

    1. image reconstruction
    2. lensless
    3. super-resolution

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