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11 March 2020 Enhancing resolution in coherent microscopy using deep learning (Conference Presentation)
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Proceedings Volume 11249, Quantitative Phase Imaging VI; 1124904 (2020) https://doi.org/10.1117/12.2545429
Event: SPIE BiOS, 2020, San Francisco, California, United States
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.
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Kevin de Haan, Tairan Liu, Yair Rivenson, Zhensong Wei, Xin Zeng, Yibo Zhang, and Aydogan Ozcan "Enhancing resolution in coherent microscopy using deep learning (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124904 (11 March 2020); https://doi.org/10.1117/12.2545429
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