We introduce GedankenNet, a self-supervised learning model for hologram reconstruction. During its training, GedankenNet leveraged a physics-consistency loss informed by the physical forward model of the imaging process, and simulated holograms generated from artificial random images with no correspondence to real-world samples. After this experimental-data-free training based on “Gedanken Experiments”, GedankenNet successfully generalized to experimental holograms on its first exposure to real-world experimental data, reconstructing complex fields of various samples. This self-supervised learning framework based on a physics-consistency loss and Gedanken experiments represents a significant step toward developing generalizable, robust and physics-driven AI models in computational microscopy and imaging.
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