Deep learning based metamaterial assisted illumination nanoscopy

Zachary Burns; Junxiang Zhao; Yeon Ui Lee; Zhaowei Liu

doi:10.1117/12.2593467

1 August 2021 Deep learning based metamaterial assisted illumination nanoscopy

Zachary Burns, Junxiang Zhao, Yeon Ui Lee, Zhaowei Liu

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118040Z (2021) https://doi.org/10.1117/12.2593467
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

The blind-SIM algorithm reconstructs super-resolution images using random illumination patterns. Recently, Speckle-MAIN has shown that this algorithm, when combined with high resolution near-field illumination patterns, can extend SIM resolution down to 40nm. However, the use of the iterative blind-SIM algorithm is computationally expensive, time-consuming and is prone to artifacts. We demonstrate that using a deep neural network we can achieve similar or better reconstruction results compared to blind-SIM with fewer artifacts and orders of magnitude better reconstruction time. This work makes real-time Speckle-MAIN super resolution imaging possible.

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Citation Download Citation

Zachary Burns, Junxiang Zhao, Yeon Ui Lee, and Zhaowei Liu "Deep learning based metamaterial assisted illumination nanoscopy", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118040Z (1 August 2021); https://doi.org/10.1117/12.2593467

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KEYWORDS

Image acquisition

Reconstruction algorithms

Metamaterials

Microscopy

Near field

Neural networks

Real time imaging

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