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Proceedings Paper

Enhancing resolution in coherent microscopy using deep learning (Conference Presentation)

Paper Abstract

We report a generative adversarial network (GAN)-based framework to super-resolve both pixel-limited and diffraction-limited images, acquired by coherent microscopy. We experimentally demonstrate a resolution enhancement factor of 2-6× for a pixel-limited imaging system and 2.5× for a diffraction-limited imaging system using lung tissue sections and Papanicolaou (Pap) smear slides. The efficacy of the technique is proven both quantitatively and qualitatively by a direct visual comparison between the network’s output images and the corresponding high-resolution images. Using this data driven technique, the resolution of coherent microscopy can be improved to substantially increase the imaging throughput.

Paper Details

Date Published: 11 March 2020
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Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124904 (11 March 2020); doi: 10.1117/12.2545429
Show Author Affiliations
Kevin de Haan, Univ. of California, Los Angeles (United States)
Tairan Liu, Univ. of California, Los Angeles (United States)
Yair Rivenson, Univ. of California, Los Angeles (United States)
Zhensong Wei, Univ. of California, Los Angeles (United States)
Xin Zeng, Univ. of California, Los Angeles (United States)
Yibo Zhang, Univ. of California, Los Angeles (United States)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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