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

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

doi:10.1038/s41377-025-01842-w

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

Original Articles

- Real-time and universal network for volumetric imaging from microscale to macroscale at high resolution
- Real-time and universal network for volumetric imaging from microscale to macroscale at high resolution
- LSA 2025年14卷第7期页码：1851-1869
- Affiliations：
  
  1.College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
  2.Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China
  3.Key Laboratory of Soybean Molecular Design Breeding, National Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
  4.Institute of Physics Chinese Academy of Sciences, Beijing, China
- Author bio：
  
  Huijun Tan (tanhuijun@nuaa.edu.cn)
  Zhijing Zhu (vczzj@zju.edu.cn)
  Depeng Wang (depeng.wang@nuaa.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-01842-w
  中图分类号：
- 收稿日期：2024-10-08，
  
  修回日期：2025-03-04，
  
  录用日期：2025-03-21，
  
  网络出版日期：2025-04-29，
  
  纸质出版日期：2025-07-31
- Accepted：
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Real-time and universal network for volumetric imaging from microscale to macroscale at high resolution[J]. LSA, 2025,14(7):1851-1869.

Lin, B Z. et al. Real-time and universal network for volumetric imaging from microscale to macroscale at high resolution. Light: Science & Applications, 14, 1851-1869 (2025).
Real-time and universal network for volumetric imaging from microscale to macroscale at high resolution[J]. LSA, 2025,14(7):1851-1869. DOI： 10.1038/s41377-025-01842-w.

Lin, B Z. et al. Real-time and universal network for volumetric imaging from microscale to macroscale at high resolution. Light: Science & Applications, 14, 1851-1869 (2025). DOI： 10.1038/s41377-025-01842-w.

摘要

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 300 μm × 300 μm × 12 μm to 25 mm × 25 mm × 25 mm

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.

关键词

Keywords

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