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REVIEW article

Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1500270
This article is part of the Research Topic Functional Devices and Biosensors View all articles

Deep learning methods for high-resolution microscale light field image reconstruction: A survey

Provisionally accepted
Depeng Wang Depeng Wang 1*Bingzhi Lin Bingzhi Lin 1Yuan Tian Yuan Tian 2Yue Zhang Yue Zhang 1Zhijing Zhu Zhijing Zhu 3
  • 1 Nanjing University of Aeronautics and Astronautics, Nanjing, China
  • 2 Duke University, Durham, North Carolina, United States
  • 3 Hangzhou City University, Hangzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    Deep learning is progressively emerging as a vital tool for image reconstruction in light field microscopy. The present review provides a comprehensive examination of the latest advancements in light field image reconstruction techniques based on deep learning algorithms. First, the review briefly introduced the concept of light field and deep learning techniques. Following that, the application of deep learning in light field image reconstruction is discussed. Subsequently, we classified deep learning-based light field microscopy reconstruction algorithms into three types based on the contribution of deep learning, including fully deep learning-based method, deep learning enhanced raw light field image with numerical inversion volumetric reconstruction, and numerical inversion volumetric reconstruction with deep learning enhanced resolution, and comprehensively analyzed the features of each approach. Finally, we discussed several challenges, including deep neural approaches for increasing the accuracy of light field microscopy to predict temporal information, methods for obtaining light field training data, strategies for data enhancement using existing data, and the interpretability of deep neural networks.

    Keywords: Deep learning1, light field microscopy2, light field imaging3, high resolution4, volumetric reconstruction5

    Received: 23 Sep 2024; Accepted: 30 Oct 2024.

    Copyright: © 2024 Wang, Lin, Tian, Zhang and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Depeng Wang, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.