Restoration Algorithm

Image restoration algorithms aim to recover high-quality images from degraded versions, addressing challenges like blur, noise, and atmospheric turbulence across diverse applications such as medical imaging, astronomy, and video processing. Current research heavily utilizes deep learning, employing architectures like generative adversarial networks (GANs), diffusion models, and neural networks optimized through techniques such as test-time optimization and self-supervised learning to achieve improved perceptual quality and robustness. These advancements significantly impact various fields by enhancing image analysis, enabling more accurate diagnoses from medical scans, and improving the quality of long-range imaging and video processing.