Deep learning-based ultrafast structured illumination microscopy imaging reconstruction

Yang Zhao; Fan Luo; Chonglei Zhang

doi:10.1117/12.2691144

16 October 2023 Deep learning-based ultrafast structured illumination microscopy imaging reconstruction

Yang Zhao, Fan Luo, Chonglei Zhang

Proceedings Volume 12792, Eighteenth National Conference on Laser Technology and Optoelectronics; 127920U (2023) https://doi.org/10.1117/12.2691144
Event: Eighteenth National Conference on Laser Technology and Optoelectronics, 2023, Shanghai, China

Abstract

Structured Illumination Microscopy (SIM) has been widely applied in the biomedical field due to its high imaging resolution and low phototoxicity. However, SIM often requires the acquisition of 9 raw images for reconstruction, which sacrifices temporal resolution. With the emergence of Compressed Sensing (CS) technology, data compression during the sampling process has accelerated imaging speed. By combining SIM and CS techniques, it is possible to further enhance the imaging speed of SIM. In this paper, we propose an algorithm based on the aforementioned concept: utilizing the physical model of CS-SIM, we simulate the generation of a pre-training dataset. Subsequently, we train a deep learning network, namely Nonlinear Activation Free Network (NAFNet), to perform ultrafast and super-resolution reconstruction. This end-to-end approach reduces the complexity of imaging, improves imaging efficiency, and significantly enhances the quality of the reconstructed images. Now, we have achieved a compression ratio of 9:1. Furthermore, the simulated data we utilized in the training process has been generalized. Despite the limited availability of biological samples, we are still able to achieve super-resolution reconstruction of biological tissues.

Citation Download Citation

Yang Zhao, Fan Luo, and Chonglei Zhang "Deep learning-based ultrafast structured illumination microscopy imaging reconstruction", Proc. SPIE 12792, Eighteenth National Conference on Laser Technology and Optoelectronics, 127920U (16 October 2023); https://doi.org/10.1117/12.2691144

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KEYWORDS

Image restoration

Biological imaging

Image processing

Deep learning

Microscopy

Ultrafast imaging

Biomedical optics

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