Multiple Degradation

Multiple degradation research focuses on restoring images or other data corrupted by multiple simultaneous impairments, such as noise, haze, rain, and blur. Current efforts concentrate on developing unified models, often employing deep learning architectures like diffusion models or neural networks with specialized modules for degradation representation and adaptive processing, aiming for all-in-one restoration capabilities. This field is significant for improving image and data quality in various applications, from infrastructure inspection (e.g., sewer pipe analysis) to enhancing the performance of computer vision systems operating in challenging real-world conditions. The development of efficient and robust methods for handling multiple degradations is crucial for advancing numerous scientific and practical domains.