Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction

Abstract

Digital in-line holographic microscopy is a computational imaging method useful for characterizing the refractive properties of a sample, i.e. the phase shift and absorption. This indirect measurement technique captures a diffraction pattern and uses reconstruction algorithms to retrieve the optical properties of the sample. Since only the intensity of the diffracted wave is recorded on the sensor, this inversion is not trivial, and simple backward propagation leads to artifacts known in optics as the ``twin-image''. With advances in deep learning, various algorithms have been developed for the reconstruction of in-line holograms, providing computationally efficient alternatives to iterative algorithms. These algorithms rely either on supervised learning, which requires ground truth knowledge, or physics-based self-supervised algorithms that require additional information, like phase diversity, but require multiple holograms for inference. This paper introduces a new self-supervised physics-based deep learning strategy that leverages phase diversity during training and then reconstructs sample's transmission function from a single in-line hologram during inference. We introduce five datasets of simulated and experimental in-line holograms of beads and bacteria. The proposed method produces accurate quantitative reconstructions similar or even more accurate than those obtained by regularized inversion while reducing the computational time by a factor of 1000.