Imaging inside highly scattering media using hybrid deep learning and analytical algorithm

Ben Wiesel, Shlomi Arnon

Research output: Contribution to journalArticlepeer-review


Imaging through highly scattering media is a challenging problem with numerous applications in biomedical and remote-sensing fields. Existing methods that use analytical or deep learning tools are limited by simplified forward models or a requirement for prior physical knowledge, resulting in blurry images or a need for large training databases. To address these limitations, we propose a hybrid scheme called Hybrid-DOT that combines analytically derived image estimates with a deep learning network. Our analysis demonstrates that Hybrid-DOT outperforms a state-of-the-art ToF-DOT algorithm by improving the PSNR ratio by 4.6 dB and reducing the resolution by a factor of 2.5. Furthermore, when compared to a deep learning stand-alone model, Hybrid-DOT achieves a 0.8 dB increase in PSNR, 1.5 times the resolution, and a significant reduction in the required dataset size (factor of 1.6–3). The proposed model remains effective at higher depths, providing similar improvements for up to 160 mean-free paths.

Original languageEnglish
JournalJournal of Biophotonics
StateAccepted/In press - 1 Jan 2023


  • computational imaging
  • deep-learning-DOT
  • diffuse tomography
  • time-of-flight

ASJC Scopus subject areas

  • Chemistry (all)
  • Materials Science (all)
  • Biochemistry, Genetics and Molecular Biology (all)
  • Engineering (all)
  • Physics and Astronomy (all)


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