Inverse Imaging

Inverse imaging aims to reconstruct high-quality images from incomplete or degraded data, a crucial task across diverse fields like medical imaging and astronomy. Current research heavily utilizes deep learning, employing architectures such as diffusion models, generative adversarial networks (GANs), and physics-informed neural networks (PINNs) to solve these often ill-posed problems. These approaches leverage both data-driven learning and the incorporation of physical constraints to improve reconstruction accuracy and efficiency, leading to advancements in various applications including medical image analysis, radio interferometry, and electromagnetic imaging. The resulting improvements in image quality and computational speed have significant implications for scientific discovery and technological applications.