Single-frame structured illumination microscopy  based on ensemble learning

Chunyao Wang; Jiaming Qian; Chao Zuo

doi:10.1117/12.3046314

26 September 2024 Single-frame structured illumination microscopy based on ensemble learning

Chunyao Wang, Jiaming Qian, Chao Zuo

Proceedings Volume 13282, Second Advanced Imaging and Information Processing Conference (AIIP 2024); 132820K (2024) https://doi.org/10.1117/12.3046314
Event: Second Advanced Imaging and Information Processing Conference (AIIP 2024), 2024, Xining, China

Abstract

Structured Illumination Microscopy (SIM) has become one of the most important fluorescence super-resolution technologies in living cell research due to its advantages of full-field imaging and low light damage. However, SIM is a multiframe imaging technique that requires complex parameter estimation, which severely limits imaging quality and speed. To address these issues, this paper proposes an ensemble learning based single-frame composite structured illumination microscopy (E-SIM). This method obtains the full-field modulation information through only one frame composite structured illumination and combines the advantages of Convolutional Neural Networks (CNNs) and Transformer to reconstruct high-quality super-resolution images from a single-frame input image, which significantly reduces phototoxicity and photobleaching. Experimental results show that E-SIM can successfully reconstruct high-resolution images in various biological samples.

Citation Download Citation

Chunyao Wang, Jiaming Qian, and Chao Zuo "Single-frame structured illumination microscopy based on ensemble learning", Proc. SPIE 13282, Second Advanced Imaging and Information Processing Conference (AIIP 2024), 132820K (26 September 2024); https://doi.org/10.1117/12.3046314

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KEYWORDS

Light sources and illumination

Quantum networks

Image restoration

Network architectures

Microscopy

Super resolution

Biological samples

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