Self-supervised learning of hologram reconstruction using physics consistency

卷:5

Article

关键词: INFORMED NEURAL-NETWORKS;    PHASE RETRIEVAL;    IMAGE-RECONSTRUCTION;    DIGITAL HOLOGRAPHY;    HIGH-RESOLUTION;    MICROSCOPY;    FRAMEWORK;    FIELD;    ENHANCEMENT;   

DOI  :  10.1038/s42256-023-00704-7

来源: SCIE

【 摘 要 】

Existing applications of deep learning in computational imaging and microscopy mostly depend on supervised learning, requiring large-scale, diverse and labelled training data. The acquisition and preparation of such training image datasets is often laborious and costly, leading to limited generalization to new sample types. Here we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labelled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks. Without prior knowledge about the sample types, the self-supervised learning model was trained using a physics-consistency loss and artificial random images synthetically generated without any experiments or resemblance to real-world samples. After its self-supervised training, GedankenNet successfully generalized to experimental holograms of unseen biological samples, reconstructing the phase and amplitude images of different types of object using experimentally acquired holograms. Without access to experimental data, knowledge of real samples or their spatial features, GedankenNet achieved complex-valued image reconstructions consistent with the wave equation in free space. The GedankenNet framework also shows resilience to random, unknown perturbations in the physical forward model, including changes in the hologram distances, pixel size and illumination wavelength. This self-supervised learning of image reconstruction creates new opportunities for solving inverse problems in holography, microscopy and computational imaging. Microscopic imaging and holography aim to decrease reliance on labelled experimental training data, which can introduce biases, be time-consuming and costly to prepare, and may lack real-world diversity. Huang et al. develop a physics-driven self-supervised model that eliminates the need for labelled or experimental training data, demonstrating superior generalization on the reconstruction of experimental holograms of various samples.

【 授权许可】

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