Phase Reconstruction

Phase reconstruction aims to recover the lost phase information of a signal, crucial for various applications like microscopy and imaging, from its measured intensity. Current research heavily utilizes deep learning, employing architectures like U-Nets, Conformers, and physics-informed neural networks to achieve this, often focusing on improving speed, accuracy, and robustness to noise in diverse data modalities (e.g., X-ray diffraction, interferometry, and audio spectrograms). These advancements enable higher-resolution imaging, faster data processing, and improved signal reconstruction in fields ranging from biomedical imaging to oceanography and speech enhancement.