Self-Supervised Deep Convolutional Reconstruction for Low-Light X-Ray Fluorescence Ghost Imaging

IEEE International Conference on Acoustics Speech and Signal Processing, pp. 21942–21946

Abstract

We recently developed a new self-supervised deep-learning-based Ghost Imaging (GI) reconstruction method, which provides unparalleled reconstruction performance for noisy acquisitions among unsupervised methods. Self-supervision removes the need for clean reference data while offering strong noise reduction. This provides the necessary tools for addressing signal-to-noise ratio concerns for GI acquisitions in emerging and cutting-edge low-light GI scenarios. Notable examples include micro- and nano-scale x-ray emission imaging, e.g., x-ray fluorescence (XRF) imaging of dosesensitive samples. Here, we will analyze the performance of our method against the state of the art in unsupervised reconstruction methods for the reconstruction of XRF-GI data.

Authors 4

1. Nicola Viganò

   Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · Université Grenoble Alpes

   Affiliation as printed

   IRIG-MEM,UGA, CEA,Grenoble,France,38000

2. Dmitry Karpov

   Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · Université Grenoble Alpes

   Affiliation as printed

   IRIG-MEM,UGA, CEA,Grenoble,France,38000

3. Kees Joost Batenburg Aachen

   Leiden University

   Affiliation as printed

   Leiden Universiteit,LIACS,CA Leiden,The Netherlands,2333

4. S. Shwartz

   Bar-Ilan University

   Affiliation as printed

   Bar Ilan University,Physics Dept.,Ramat Gan,Israel,52900