Chinese Optics Letters, Volume. 22, Issue 1, (2024)

Differential Interference Contrast Phase Edging Net: an all-optical learning system for edge detection of phase objects [Early Posting]

Li Yiming, Li Ran, Chen Quan, Luan Haitao , Lu Haijun, Yang Hui, Gu Min, Zhang Qiming
Author Affiliations
  • University of Shanghai for Science and Technology
  • Nokia Shanghai Bell Co LTD
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    Edge detection for low-contrast phase objects cannot be performed directly by spatial difference of intensity distribution. In this work, an all-optical diffractive neural network (DPENet) based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner has been proposed. Edge information is encoded into interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges. Results show that DPENet achieves F-scores of 0.9293 (MNIST) and 0.9356 (NIST) and enables real-time edge detection of biological cells, achieving an F-score of 0.7547.

    Paper Information

    Manuscript Accepted: Aug. 14, 2023

    Posted: Sep. 18, 2023

    DOI: COL-0576