A hybrid reconstruction of the physical model with the deep-learning that improves structured illumination microscopy
biorxiv(2022)
摘要
In handling raw images with low signal-to-noise (SNR) ratios, conventional algorithms of structured illumination microscopy are prone to artifacts, while deep-learning-based (DL) algorithms may lead to degradation and hallucinations. We propose a hybrid that combines the physical inversion model with a Total Deep Variation regularization. In super-resolving from low SNR images such as actin filaments, our method outperforms conventional or DL methods in suppressing artifacts and hallucinations while maintaining resolutions.
### Competing Interest Statement
The authors have declared no competing interest.
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关键词
structured illumination microscopy,hybrid reconstruction,physical model,deep-learning
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