Low-Light Image Enhancement with Normalizing Flow

By Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, Alex ChiChung Kot. AAAI Conference on Artificial Intelligence, 2021

590 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4abede791129ff4eb42b91c9b1091c27f432114d

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In Computer Vision and Pattern Recognition

Citations in the corpus

Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method (2022, methodology): “Compared with CNN-based supervised learning methods, the performances of transformer-based supervised learning methods (Uformer, Restormer, and LLFormer) are greatly improved.”
SinSR: Diffusion-Based Image Super-Resolution in a Single Step (2023, background): “Subsequently, normalizing flows [23, 42] are demonstrated to have good perceptual quality under an efficient inference process, while its network design is restricted by the requirements of the invertibility and ease of calculation.”