Enhancement of Partially Coherent Diffractive Images Using Generative Adversarial Network

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2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)274-284
Number of pages11
JournalAI (Switzerland)
Volume3
Issue number2
DOIs
StatePublished - Jun 2022

Keywords

  • coherent diffractive imaging
  • GAN (generative adversarial network)
  • partial coherence
  • phase retrieval
  • ptychography

ASJC Scopus subject areas

  • Artificial Intelligence

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