RT-X Net: RGB-Thermal cross attention network for Low-Light Image Enhancement

Paper

Code

V-TIEE Dataset

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.

Method

The overview of our method:
(a) In the RT-X Net, Input RGB and Thermal images are fed to the illumination estimator, from which it gets the image features, and those features are used for self-attention, and then these features will be fused using cross cross-attention network to produce an enhanced low-light image.

(b) The workflow of the Self-attention network
(c) The architecture of the cross-attention network to fuse the features of the RGB and thermal images

Results

Qualitative results on the synthetic LLVIP dataset and real-world V-TIEE dataset. Columns denote different scenes. The first two rows show the input visible and thermal images. The next five rows are the outputs from RT-X Net and state-of-the-art visible image enhancement algorithms. The last row shows the reference well-exposed image.

V-TIEE Dataset:

Real-world V-TIEE Dataset: Co-located Visible-Thermal Image Pairs for HDR and Low-light Vision Research

Citations:

If you find our code or datasets useful in your research, please cite the following:

@misc{jha2025rtxnetrgbthermalcross,

title={RT-X Net: RGB-Thermal cross attention network for Low-Light Image Enhancement},

author={Raman Jha and Adithya Lenka and Mani Ramanagopal and Aswin Sankaranarayanan and Kaushik Mitra},

year={2025},

eprint={2505.24705},

archivePrefix={arXiv},

primaryClass={cs.CV},

url={https://arxiv.org/abs/2505.24705},

}

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