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Fan Luo

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Proceedings Article | 16 October 2023 Paper

Deep learning-based ultrafast structured illumination microscopy imaging reconstruction

Yang Zhao, Fan Luo, Chonglei Zhang

Proceedings Volume 12792, 127920U (2023) https://doi.org/10.1117/12.2691144

KEYWORDS: Image restoration, Biological imaging, Image processing, Deep learning, Microscopy, Ultrafast imaging, Biomedical optics, Super resolution, Compressed sensing

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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.

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