Bilevel Fast Scene Adaptation for Low-Light Image Enhancement

Understand

Enhancing images in low-light scenes is a challenging but widely concerned task in the computer vision.

• The mainstream learning-based methods mainly acquire the enhanced model by learning the data distribution from the specific scenes, causing poor adaptability (even failure) when meeting real-world scenarios that have never been encountered before.
• The main obstacle lies in the modeling conundrum from distribution discrepancy across different scenes.
• To remedy this, we first explore relationships between diverse low-light scenes based on statistical analysis, i.e., the network parameters of the encoder trained in different data distributions are close.