Image Restoration

Image restoration estimates cleaner or more complete images from degraded measurements, supporting applications from photography and remote sensing to medical and scientific imaging. Methods range from inverse-problem and physics-based models to CNNs, Transformers, state-space networks, and diffusion or flow models; self-supervised learning and pretrained priors help when clean paired examples are scarce. Work increasingly targets unified models for mixed degradations, real-world adaptation, and efficient deployment, with evaluation extending beyond image metrics to downstream tasks such as detection and diagnosis. A central challenge is balancing fidelity to observed evidence with perceptual quality: generative methods can add plausible but incorrect detail, so reliable control and hallucination assessment remain important.