Collaborative brightening and amplification of low-light imagery via bi-level adversarial learning

Jiaxin Gao, Yaohua Liu, Ziyu Yue, Xin Fan, Risheng Liu

Abstract: Highlights•We present CollaBA, a specialized dual-path modulated-aggregated enhancer, as a fresh approach to tackling the intricate challenge of collaborating amplification and brightening images taken in extremely low-light conditions.•Our CollaBA gains remarkable performance by imposing generative modulation priors to guide exposure regulation, progressively integrating them into the multi-scale degradation removal branch through spatial feature transformation.•Instead of naive time-consuming adversarial learning strategy, a novel bi-level implicit adversarial learning mechanism is designed, effectively improving the stability of training and the quality of visual perception.•Extensive experiments were conducted to thoroughly validate that our method surpasses existing state-of-the-art approaches on real-world benchmark datasets, particularly in extremely low-light conditions, achieving a 35.8% improvement in LPIPS and a 23.1% increase in RMSE.