Recent developments in the field of deep learning have opened up exciting avenues for significantly advancing holography and coherent imaging systems by circumventing some of these challenges of coherent imaging systems while taking full advantage of their inherent benefits. The wide applicability of digital holographic microscopes is partially bottlenecked by some challenges: the “missing phase problem” in holography requires phase recovery, which is often implemented using iterative methods that demand the acquisition of additional measurements using relatively complex and alignment-sensitive imaging set-ups furthermore, even after the phase recovery step, coherence-related artifacts appear in the reconstructed images in the form of, e.g., speckle noise and multiple-reflection interference, which altogether degrade the image contrast compared to, e.g., brightfield or fluorescence microscopy. Despite these important advantages, digital holographic microscopy systems are not as widely used as other microscopy modalities, such as brightfield or fluorescence microscopes. Some important advantages of holography include label-free imaging of samples at a low-radiation dose, inference of the objects’ phase distribution (especially useful for the imaging of, e.g., live cells and other biological specimens within a liquid environment) 1, 2, and numerical 3D refocusing throughout the sample volume by processing a single hologram, i.e., without any mechanical scanning. Among these computational microscopy modalities, digital holographic microscopy (DHM) provides several unique opportunities by encoding a complex 3D optical field into intensity modulations through the interference of scattered sample waves and a reference wave, which forms a hologram of the sample. Exponential advancements in computational resources and algorithms have given rise to new paradigms in microscopic imaging modalities that rely on computation to digitally reconstruct and enhance images, surpassing the capabilities of conventional microscopes.
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