OAH-Net: A Deep Neural Network for Hologram Reconstruction of Off-axis Digital Holographic Microscope

Digital holographic microscopy (DHM) is emerging as an innovative imaging modality in computational microscopy. It provides high-resolution, quantitative, and threedimensional information about samples without labelling. These unique features make DHM a promising technique for imaging living cells, as it captures intracellular structures while preserving cells in their natural state, which could be used for more precise analysis [1-4]. DHM records the interference pattern between the object and the reference beams, which is then reconstructed using algorithms to retrieve the wave of the object beam in terms of phase and amplitude [5-7]. Holography can be classified into two main types based on beam alignment: inline holography and off-axis holography [8, 9]. For inline holography, the reference beam is parallel to the object beam. Although the setup is relatively simple, the hologram reconstruction requires multiple exposures of the same sample at varying sample-to-sensor distances.