Image Quality Recovery

Image quality recovery focuses on restoring degraded or incomplete images to a higher quality state, addressing issues like rain streaks, low light conditions, and low-dose radiation in medical imaging. Current research emphasizes the development of sophisticated deep learning models, including generative adversarial networks (GANs), diffusion models, and transformer-based architectures, often incorporating multi-modal information (e.g., text and image) or hierarchical structures to improve accuracy and efficiency. These advancements have significant implications for various fields, improving the diagnostic capabilities of medical imaging, enhancing the visual quality of consumer photos and videos, and bolstering data security through improved steganography techniques.