SPACE: Structure-Preserving Cross-Modal Image Enhancement for Extreme Low-Light Conditions

Abstract

Low-Light Image Enhancement (LLIE) remains challenging due to severe noise, color artifacts, and structural degradation in extreme low-light environments. Existing methods primarily rely on photometric cues and often fail to preserve geometric consistency, resulting in blurred edges and distorted textures. To address this limitation, we introduce LAMP, a large-scale dataset of Low-light Aligned Multimodal Pairs for LLIE. LAMP contains over 18k high-quality aligned pairs and consists of two complementary subsets. LAMP-Real is captured using LiDAR-based depth sensors under physically controlled illumination, while LAMP-Synthetic is generated through a physics-calibrated degradation pipeline. Both subsets provide precisely aligned RGB images, depth maps, and pixel-wise semantic annotations. Building upon LAMP, we propose SPACE, a Structure Preservation Aware Cross-modal Enhancement framework that explicitly leverages geometric priors. SPACE introduces a Depth-Adaptive HVI Transformation to decouple luminance and chrominance under depth guidance, effectively suppressing color-space noise. Furthermore, a Depth-Manifold Modulated Attention mechanism constrains feature interactions within a learned depth manifold, ensuring structural coherence during enhancement. Extensive experiments demonstrate that SPACE consistently outperforms state-of-the-art methods in visual quality and structural fidelity. The code and data are available at https://github.com/YueCheong/SPACE.