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Abstract— <p>Imaging of physical objects using single-pixel cameras is an actively developing area at the intersection of optics and computational mathematics. Image restoration algorithms used in single-pixel cameras usually provide low resolution due to practical limitations on realistic computing resources. In this paper, we demonstrate an increase in the resolution of images obtained in single-pixel imaging using a generative adversarial neural network and discuss its application using the example of chest X-ray images from the MedMNIST dataset.</p>

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Image Superresolution in Single-Pixel Imaging with Generative Adversarial Networks

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • D. V. Babukhin,
  • A. A. Reutov,
  • D. V. Sych

摘要

Abstract—

Imaging of physical objects using single-pixel cameras is an actively developing area at the intersection of optics and computational mathematics. Image restoration algorithms used in single-pixel cameras usually provide low resolution due to practical limitations on realistic computing resources. In this paper, we demonstrate an increase in the resolution of images obtained in single-pixel imaging using a generative adversarial neural network and discuss its application using the example of chest X-ray images from the MedMNIST dataset.