Quantitative Phase

Quantitative phase imaging and analysis are crucial for extracting information from the phase component of signals or images, offering label-free insights into various systems. Current research focuses on improving the speed and accuracy of phase retrieval using techniques like deep learning-based neural networks, including novel architectures designed for efficient model selection and phase reconstruction from limited data. These advancements are impacting diverse fields, from materials science and biology (via faster, more efficient microscopy) to speech recognition (via improved spoof detection) and fundamental physics (via improved S-matrix analysis). The development of optimized algorithms and hardware is driving significant improvements in both the speed and accuracy of quantitative phase measurements.