Low-Light Image Enhancement with Normalizing Flow


  • Yufei Wang Nanyang Technological University
  • Renjie Wan Hong Kong Baptist University
  • Wenhan Yang Nanyang Technological University
  • Haoliang Li City University of Hong Kong
  • Lap-Pui Chau Nanyang Technological University
  • Alex Kot Nanyang Technological University




Computer Vision (CV), Machine Learning (ML)


To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to capture the complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, and artifacts. In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. In this way, the conditional distribution of the normally exposed images can be well modeled, and the enhancement process, i.e., the other inference direction of the invertible network, is equivalent to being constrained by a loss function that better describes the manifold structure of natural images during the training. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.




How to Cite

Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.-P., & Kot, A. (2022). Low-Light Image Enhancement with Normalizing Flow. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3), 2604-2612. https://doi.org/10.1609/aaai.v36i3.20162



AAAI Technical Track on Computer Vision III