融合可见光与热成像，提升夜间低光照图像质量。
RT-X Net: RGB-Thermal cross attention network for Low-Light Image Enhancement
- 用交叉注意力机制融合RGB与热成像特征。
- 在LLVIP和自建V-TIEE数据集上超越现有方法。
- 适合做夜间视觉增强、多模态图像处理的研究者。
夜间环境下，高噪声和强光源导致图像质量下降，低光照图像增强面临挑战。热成像提供互补信息，具备更丰富的纹理和结构细节。本文提出RT-X Net，一种基于交叉注意力的网络，用于融合可见光与热成像以实现夜间图像增强。通过自注意力网络提取特征，并利用交叉注意力机制实现双模态信息的有效融合。为支持该领域研究，我们构建了可见-热图像增强评估数据集V-TIEE，包含50对在多种夜间条件下同步采集的可见光与热成像图像。在公开的LLVIP数据集及自建V-TIEE数据集上的大量实验表明，RT-X Net在低光照图像增强任务中优于当前最先进方法。代码与数据集详见https://github.com/jhakrraman/rt-xnet。
原文摘要 · Abstract (English)
In nighttime conditions, high noise levels and bright illumination sources degrade image quality, making low-light image enhancement challenging. Thermal images provide complementary information, offering richer textures and structural details. We propose RT-X Net, a cross-attention network that fuses RGB and thermal images for nighttime image enhancement. We leverage self-attention networks for feature extraction and a cross-attention mechanism for fusion to effectively integrate information from both modalities. To support research in this domain, we introduce the Visible-Thermal Image Enhancement Evaluation (V-TIEE) dataset, comprising 50 co-located visible and thermal images captured under diverse nighttime conditions. Extensive evaluations on the publicly available LLVIP dataset and our V-TIEE dataset demonstrate that RT-X Net outperforms state-of-the-art methods in low-light image enhancement. The code and the V-TIEE can be found here https://github.com/jhakrraman/rt-xnet.
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