Based Enhancement Model

Based enhancement models aim to improve the quality of various data types, including images and audio, by learning to reverse degradation processes. Current research focuses on developing models that offer controllable and continuous enhancement, often leveraging architectures like U-Nets and diffusion models, and incorporating techniques such as quality-guided learning and data augmentation. These advancements have significant implications for diverse fields, improving performance in tasks such as fingerprint recognition, medical image analysis, and object detection in challenging conditions.