Light: Science & Applications (Nov 2023)

E2E-BPF microscope: extended depth-of-field microscopy using learning-based implementation of binary phase filter and image deconvolution

  • Baekcheon Seong,
  • Woovin Kim,
  • Younghun Kim,
  • Kyung-A Hyun,
  • Hyo-Il Jung,
  • Jong-Seok Lee,
  • Jeonghoon Yoo,
  • Chulmin Joo

DOI
https://doi.org/10.1038/s41377-023-01300-5
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 14

Abstract

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Abstract Several image-based biomedical diagnoses require high-resolution imaging capabilities at large spatial scales. However, conventional microscopes exhibit an inherent trade-off between depth-of-field (DoF) and spatial resolution, and thus require objects to be refocused at each lateral location, which is time consuming. Here, we present a computational imaging platform, termed E2E-BPF microscope, which enables large-area, high-resolution imaging of large-scale objects without serial refocusing. This method involves a physics-incorporated, deep-learned design of binary phase filter (BPF) and jointly optimized deconvolution neural network, which altogether produces high-resolution, high-contrast images over extended depth ranges. We demonstrate the method through numerical simulations and experiments with fluorescently labeled beads, cells and tissue section, and present high-resolution imaging capability over a 15.5-fold larger DoF than the conventional microscope. Our method provides highly effective and scalable strategy for DoF-extended optical imaging system, and is expected to find numerous applications in rapid image-based diagnosis, optical vision, and metrology.