Abstract
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 this https URL.
Abstract (translated)
在夜间条件下，高噪音水平和明亮的照明源会降低图像质量，使得低光环境下的图像增强变得极具挑战性。热成像图提供互补信息，可以提供更多纹理和结构细节。我们提出了RT-X Net，这是一种交叉注意力网络，用于融合RGB和热图以进行夜间图像增强。我们利用自注意网络提取特征，并采用跨模态注意力机制将两种模式的信息有效集成起来。为了支持该领域的研究，我们引入了可见光-热成像图像增强评估（V-TIEE）数据集，包含在各种夜间条件下拍摄的50组位置对应的可见光和热图。通过公开可用的LLVIP数据集以及我们的V-TIEE数据集进行广泛测试后发现，RT-X Net 在低光照图像增强方面优于现有技术方法。该代码及V-TIEE数据集可在此网址获得：[此链接]（请将"[此链接]"替换为实际链接）。
URL
https://arxiv.org/abs/2505.24705
