Digital Holography

Digital holography is a three-dimensional imaging technique that reconstructs objects from their diffraction patterns, aiming to improve image quality and speed while reducing data requirements. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks, transformers, and generative adversarial networks to enhance reconstruction accuracy, automate focusing, and achieve super-resolution, often incorporating physics-based constraints for improved generalization. These advancements are significantly impacting fields like microscopy, particularly in biomedical imaging and materials science, by enabling faster, higher-resolution, and label-free analysis of biological samples and other microscopic structures.