UAV-LIENet: a low-light UAV image enhancement network via illumination estimation and guidance

Abstract

Low-light Unmanned Aerial Vehicle (UAV) image enhancement is crucial for downstream tasks such as object detection and navigation. However, low-light UAV images often have complex illumination patterns and high noise levels. Existing mainstream low-light enhancement methods tend to introduce color cast and local overexposure on such images. To address these issues, we propose a Low-light UAV image enhancement network named UAV-LIENet, and we train it with three progressive sub-networks. UAV-LIENet first applies a Non-uniform Luminance Estimation Network (NLEN) to reconstruct a smooth and uniform illumination component. NLEN adopts quantile-clipping normalization and a parallel coarse-and-fine architecture for illumination estimation. Then, UAV-LIENet performs adaptive denoising and color restoration under the guidance of the estimated luminance component. For accurate and stable color restoration, we design an illumination-guided saturation constraint loss, which adaptively constrains saturation in the HSV space to reduce color cast and suppress oversaturation. To evaluate our method systematically, we build a low-light enhancement dataset named UAV-LLIE based on high-fidelity game-engine rendering. UAV-LLIE contains 6 typical aerial scenarios and 6,000 pixel-aligned image pairs. Experiments show that UAV-LIENet outperforms representative existing methods in both quantitative metrics and visual quality.