Abstract

Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, constrained by the physical principles of optical imaging, volume imaging has to balance imaging resolution and speed. Here, we develop a new, to the best of our knowledge, 3D deep learning network based on a dual generative adversarial network (dual-GAN) framework for recovering high-resolution (HR) volume images from high speed acquired low-resolution (LR) volume images. The proposed method does not require a precise image registration process and meanwhile guarantees the predicted HR volume image faithful to its corresponding LR volume image. The results demonstrated that our method can recover ${20} {\times} /1.0\text-{\rm NA}$ volume images from coarsely registered ${5} {\times} /0.16\text-{\rm NA}$ volume images collected by light-sheet microscopy. This method would provide great potential in applications which require high resolution volume imaging.

© 2020 Optical Society of America

Registration-free 3D super-resolution generative deep-learning network for fluorescence microscopy imaging

Hang Zhou, Yuxin Li, Bolun Chen, Hao Yang, Maoyang Zou, Wu Wen, Yayu Ma, and Min Chen
Opt. Lett. 48(23) 6300-6303 (2023)

High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network

Hao Zhang, Chunyu Fang, Xinlin Xie, Yicong Yang, Wei Mei, Di Jin, and Peng Fei
Biomed. Opt. Express 10(3) 1044-1063 (2019)

Deep neural network inversion for 3D laser absorption imaging of methane in reacting flows

Chuyu Wei, Kevin K. Schwarm, Daniel I. Pineda, and R. Mitchell Spearrin
Opt. Lett. 45(8) 2447-2450 (2020)

Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration

Lejia Hu, Shuwen Hu, Wei Gong, and Ke Si
Opt. Lett. 46(9) 2055-2058 (2021)

Exceeding the limits of 3D fluorescence microscopy using a dual-stage-processing network

Hao Zhang, Yuxuan Zhao, Chunyu Fang, Guo Li, Meng Zhang, Yu-Hui Zhang, and Peng Fei
Optica 7(11) 1627-1640 (2020)