近日，南京航空航天大学Wang, Depeng团队研究了用于从微尺度到宏观尺度高分辨率体积成像的实时通用网络。2025年4月29日出版的《光：科学与应用》杂志发表了这项成果。

光场成像在各个领域都有广泛的应用，包括微尺度生命科学成像、中尺度神经成像和宏观尺度流体动力学成像。基于深度学习的重建方法的发展极大地促进了高分辨率光场图像处理，然而，目前基于深度学习光场重建方法主要集中在微观尺度上。考虑到光场技术的多尺度成像能力，一个可以在不同尺度的光场图像重建上工作的网络将显著有利于体积成像的发展。

不幸的是，据研究组所知，还没有人报道过一种通用的高分辨率光场图像重建算法，该算法与微尺度、中尺度和宏观尺度兼容。为了填补这一空白，他们提出了一种实时通用网络（RTU Net）来重建任何尺度的高分辨率光场图像。RTU-Net作为第一个在多尺度光场图像重建上工作的网络，采用了基于生成对抗理论的自适应损失函数，因此表现出很强的泛化能力。

研究组通过重建多尺度光场图像，包括微尺度微管蛋白和线粒体数据集、中尺度合成小鼠神经数据集和宏观尺度光场粒子成像测速数据集，全面评估了RTU Net的性能。 结果表明，RTU Net实现了300μm体积范围内的实时高分辨率光场图像重建 μm × 300 μm × 12 μm至25 mm × 25 mm × 25 并且与最近报道的光场重建网络相比显示出更高的分辨率。RTU网络的高分辨率、强鲁棒性、高效率，特别是其普遍适用性，将大大加深人们对高分辨率和体积成像的理解。

附：英文原文

Title: Real-time and universal network for volumetric imaging from microscale to macroscale at high resolution

Author: Lin, Bingzhi, Xing, Feng, Su, Liwei, Wang, Kekuan, Liu, Yulan, Zhang, Diming, Yang, Xusan, Tan, Huijun, Zhu, Zhijing, Wang, Depeng

Issue&Volume: 2025-04-29

Abstract: Light-field imaging has wide applications in various domains, including microscale life science imaging, mesoscale neuroimaging, and macroscale fluid dynamics imaging. The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing, however, current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale. Considering the multiscale imaging capacity of light-field technique, a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging. Unfortunately, to our knowledge, no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale, mesoscale, and macroscale. To fill this gap, we present a real-time and universal network (RTU-Net) to reconstruct high-resolution light-field images at any scale. RTU-Net, as the first network that works over multiscale light-field image reconstruction, employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability. We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images, including microscale tubulin and mitochondrion dataset, mesoscale synthetic mouse neuro dataset, and macroscale light-field particle imaging velocimetry dataset. The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25mm×25mm×25mm, and demonstrated higher resolution when compared with recently reported light-field reconstruction networks. The high-resolution, strong robustness, high efficiency, and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.

DOI: 10.1038/s41377-025-01842-w

Source: https://www.nature.com/articles/s41377-025-01842-w