Incomplete Imaging Data

Incomplete imaging data, a pervasive challenge across diverse fields from medical imaging to satellite remote sensing, hinders accurate analysis and interpretation. Current research focuses on developing robust methods to reconstruct missing information, employing techniques like deep learning architectures (e.g., U-Nets, diffusion models) and reinforcement learning, often incorporating prior knowledge (e.g., shape models, text descriptions) to guide the reconstruction process. These advancements aim to improve the reliability and efficiency of analyses performed on incomplete datasets, impacting applications ranging from medical diagnosis and treatment planning to environmental monitoring and resource management.