Image Enhancement

Research Overview – Image Enhancement

Image enhancement seeks to restore visual clarity under challenging conditions such as low light, noise, blur, or complex real-world degradations. It plays a crucial role in improving downstream perception tasks and visual understanding.

Our group advances this field through two core directions:

• Unsupervised Learning Methods – enhancing images without paired ground truth, enabling scalable real-world deployment.

• Multi-Degradation Handling – building models that can adapt to diverse degradations (e.g., low-light, noise, haze) within a unified framework.