Lin Bingzhi, Tian Yuan, Zhang Yue, Zhu Zhijing, Wang Depeng
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Department of Biomedical Engineering, Duke University, Durham, NC, United States.
Front Bioeng Biotechnol. 2024 Nov 18;12:1500270. doi: 10.3389/fbioe.2024.1500270. eCollection 2024.
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 was 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.
深度学习正逐渐成为光场显微镜中图像重建的重要工具。本综述全面考察了基于深度学习算法的光场图像重建技术的最新进展。首先，综述简要介绍了光场和深度学习技术的概念。在此之后，讨论了深度学习在光场图像重建中的应用。随后，我们基于深度学习的贡献将基于深度学习的光场显微镜重建算法分为三种类型，包括基于完全深度学习的方法、通过数值反演体积重建增强原始光场图像的深度学习方法以及通过深度学习增强分辨率的数值反演体积重建方法，并全面分析了每种方法的特点。最后，我们讨论了几个挑战，包括提高光场显微镜预测时间信息准确性的深度神经方法、获取光场训练数据的方法、使用现有数据进行数据增强的策略以及深度神经网络的可解释性。
