Data and Prior-Driven Low-Light Enhancement Boosting the Visibility of Imaging Systems

Abstract

Imaging systems working under poor lighting conditions often suffer from poor visibility, compromising the reliability of vision-based systems. To address this problem, recent studies have developed data-driven low-light image enhancement (LIE) techniques to improve visibility. However, these LIE methods typically require large amounts of training samples, and the learned representation may not be valid for real-world scenes due to data discrepancies. In this work, we propose DP-LIE, an unsupervised LIE method driven by both data and priors. Unlike the existing methods that learn unexplainable high-dimensional features for end-to-end mapping, DP-LIE focuses on learning prior-guided parametric maps with definite meanings, enabling the low-light images to be brightened from an interpretable prior-based perspective. To this end, we design a simple yet effective prior-guided network-assisted LIE formulation, which elaborately bonds the data-driven representations with the traditional priors. The embedded priors narrow the solution space of the LIE model, allowing it to be efficiently trained with fewer samples. Notably, even trained with just a single low-light input image, the proposed method (denoted as DP-LIE-S) achieves comparable performances with existing unsupervised LIE methods. Moreover, experiments demonstrate that the proposed DP-LIE method exhibits excellent generalization performance across diverse imaging devices and promises better detection results for vision-based detection systems in nighttime scenes.