A Multimodal Intrinsics-Guided Thermal-Aware Framework for RGB Low-Light Image Enhancement
TL;DR
A Multimodal Intrinsics-Guided Framework that integrates RGB and thermal data to reconstruct well-lit images and demonstrates strong generalization to real-world benchmarks such as LLVIP and V-TIEE, outperforming state-of-the-art methods in most evaluation metrics.
Abstract
Low-light image enhancement is crucial in situations where visible sensors might suffer from severe noise and information loss ( e.g., nighttime surveillance). Recent approaches investigate auxiliary modalities invariant to illumination to improve the performance, such as thermal infrared imaging. We propose a Multimodal Intrinsics-Guided Framework that integrates RGB and thermal data to reconstruct well-lit images. Our method utilizes a two-stage pipeline: first, we employ an intrinsic decomposition strategy to separate re-flectance and shading components through knowledge distillation, where a teacher network guides a student model in re-constructing consistent intrinsic components; then, a refine-ment stage restores fine structures and visual details. We train the proposed model on synthetic data from HDRT dataset and demonstrate strong generalization to real-world benchmarks such as LLVIP and V-TIEE, outperforming state-of-the-art methods in most evaluation metrics. Code is available at : https://github.com/simonemelc/TIRGlow