
OAH-Net is a novel deep neural network architecture that leverages the physical principles of off-axis holography and weakly supervised learning to achieve real-time, accurate, and generalizable hologram reconstruction for high-throughput digital holographic microscopy applications.


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OAH-Net: A Deep Neural Network for Fast and Accurate Hologram Reconstruction in Off-Axis Digital Holographic Microscopy


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The NU-FDS algorithm enables fast and accurate reconstruction of ultra-wide-angle computer-generated holograms (CGHs) by using non-uniform frequency magnification to correct the axial distance of parabolic waves, allowing for the application of the Fresnel Transform (FrT) method.


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Non-Uniform Fourier Domain Stretching (NU-FDS): A Novel Algorithm for Fast and Accurate Reconstruction of Ultra-Wide-Angle Computer-Generated Holograms
