Resolution enhancement in coherent imaging systems using a deep neural network

Tairan Liu; Kevin de Haan; Yair Rivenson; Zhensong Wei; Xin Zeng; Yibo Zhang; Aydogan Ozcan

doi:10.1117/12.2567519

20 August 2020 Resolution enhancement in coherent imaging systems using a deep neural network

Tairan Liu, Kevin de Haan, Yair Rivenson, Zhensong Wei, Xin Zeng, Yibo Zhang, Aydogan Ozcan

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690N (2020) https://doi.org/10.1117/12.2567519
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

We present a super-resolution framework for coherent imaging systems using a generative adversarial network. This framework requires a single low-resolution input image, and in a single feed-forward step it performs resolution enhancement. To validate its efficacy, both a lensfree holographic imaging system with a pixel-limited resolution and a lens-based holographic imaging system with diffraction-limited resolution were used. We demonstrated that for both the pixel-limited and diffraction-limited coherent imaging systems, our method was able to effectively enhance the image resolution of the tested biological samples. This data-driven super resolution framework is broadly applicable to various coherent imaging systems.

Conference Presentation

Citation Download Citation

Tairan Liu, Kevin de Haan, Yair Rivenson, Zhensong Wei, Xin Zeng, Yibo Zhang, and Aydogan Ozcan "Resolution enhancement in coherent imaging systems using a deep neural network", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690N (20 August 2020); https://doi.org/10.1117/12.2567519

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