Interferometric Imaging

Interferometric imaging techniques leverage the interference of light waves to reconstruct images with high resolution and depth information, primarily aiming to overcome limitations of traditional imaging methods. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), U-Nets, and diffusion probabilistic models to improve image reconstruction accuracy and speed, particularly in addressing challenges like noise, limited data, and varying visibility coverage. These advancements are significantly impacting fields ranging from astronomy and remote sensing to microscopy and 3D urban mapping, enabling more precise measurements and analyses in diverse scientific and engineering applications.