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