deep learning phase retrieval algorithms

Deep learning phase retrieval algorithms leverage neural networks to reconstruct an image or signal from incomplete or indirect measurements, typically when phase information is missing in optical or X-ray systems. They are trained on large datasets to learn complex mappings from measured data to the original signals, effectively bypassing traditional iterative algorithms that can be slow or sensitive to noise. These methods enable faster, more robust reconstructions, improving imaging quality in applications like microscopy, astronomy, and medical imaging, by harnessing data-driven patterns to efficiently solve the challenging inverse problem of phase retrieval.