Image Estimation

Image estimation focuses on reconstructing high-quality images from incomplete or degraded data, a crucial task across diverse fields. Current research emphasizes deep learning approaches, employing architectures like deep neural networks (DNNs) and implicit neural representations, often within iterative refinement schemes such as Richardson-Lucy deconvolution or residual-to-residual processing. These methods are applied to various challenges, including low-light deblurring, parallel MRI reconstruction, and depth estimation from single images or videos, improving accuracy and efficiency compared to traditional techniques. The resulting advancements have significant implications for medical imaging, astronomy, autonomous driving, and other applications requiring high-fidelity image reconstruction.