phase retrieval with deep learning

Phase retrieval is a technique used to reconstruct images or signals from incomplete or noisy measurements, particularly in fields like optics and imaging. In deep learning, this process leverages neural networks to learn how to infer the missing information, known as the "phase," from available data, such as intensities of light captured by sensors. By training on large datasets, these networks can identify patterns and restore high-quality images or signals, making phase retrieval more efficient and accurate compared to traditional methods. This has applications in medical imaging, astronomy, and photography, enhancing our ability to see and understand complex information.