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Deep learning-based single-shot structured illumination microscopy
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DOI：10.1016/j.optlaseng.2022.107066.png)
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摘要
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We report a deep learning-based structured illumination microscopy (SIM) method, which can reconstruct a super-resolution (SR) image using only one frame structured illumination image. Generative adversative networks (GANs) and deformation of U-Net (DU-Net) are employed to perform the task. GANs are trained to generate other structured illumination images by feeding a single structured illumination image, and DU-Net is trained to reconstruct the super-resolution image. The results of experiments and simulations demonstrate that the SR image could be reconstructed from one frame structured illumination image. Importantly, it can greatly reduce phototoxicity and photobleaching.
Keyword:
Structured illumination microscopy
Super-resolution
Deep learning
期刊
IF:
3.7
论文数:
7.3K
被引数:
1.7W
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引用论文
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