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Enhancement of Partially Coherent Diffractive Images Using Generative Adversarial Network

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Biodesign Beus CXFEL Laboratory, Arizona State University, Tempe, AZ 85281, USA
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Biodesign Center for Applied Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
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Department of Physics, Arizona State University, Tempe, AZ 85287, USA
*
Author to whom correspondence should be addressed.
Academic Editors: José Manuel Ferreira Machado and Kenji Suzuki
AI 2022, 3(2), 274-284; https://doi.org/10.3390/ai3020017
Received: 2 March 2022 / Revised: 29 March 2022 / Accepted: 7 April 2022 / Published: 11 April 2022
(This article belongs to the Special Issue Feature Papers for AI)
We present a deep learning-based generative model for the enhancement of partially coherent diffractive images. In lensless coherent diffractive imaging, a highly coherent X-ray illumination is required to image an object at high resolution. Non-ideal experimental conditions result in a partially coherent X-ray illumination, lead to imperfections of coherent diffractive images recorded on a detector, and ultimately limit the capability of lensless coherent diffractive imaging. The previous approaches, relying on the coherence property of illumination, require preliminary experiments or expensive computations. In this article, we propose a generative adversarial network (GAN) model to enhance the visibility of fringes in partially coherent diffractive images. Unlike previous approaches, the model is trained to restore the latent sharp features from blurred input images without finding coherence properties of illumination. We demonstrate that the GAN model performs well with both coherent diffractive imaging and ptychography. It can be applied to a wide range of imaging techniques relying on phase retrieval of coherent diffraction patterns. View Full-Text
Keywords: partial coherence; coherent diffractive imaging; GAN (generative adversarial network); phase retrieval; ptychography partial coherence; coherent diffractive imaging; GAN (generative adversarial network); phase retrieval; ptychography
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MDPI and ACS Style

Kim, J.W.; Messerschmidt, M.; Graves, W.S. Enhancement of Partially Coherent Diffractive Images Using Generative Adversarial Network. AI 2022, 3, 274-284. https://doi.org/10.3390/ai3020017

AMA Style

Kim JW, Messerschmidt M, Graves WS. Enhancement of Partially Coherent Diffractive Images Using Generative Adversarial Network. AI. 2022; 3(2):274-284. https://doi.org/10.3390/ai3020017

Chicago/Turabian Style

Kim, Jong W., Marc Messerschmidt, and William S. Graves. 2022. "Enhancement of Partially Coherent Diffractive Images Using Generative Adversarial Network" AI 3, no. 2: 274-284. https://doi.org/10.3390/ai3020017

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