NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset
<|reference_start|>NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset: Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments. Leveraging near-infrared (NIR) images to assist visible RGB image denoising shows the potential to address this issue, becoming a promising technology. Nonetheless, existing works still struggle with taking advantage of NIR information effectively for real-world image denoising, due to the content inconsistency between NIR-RGB images and the scarcity of real-world paired datasets. To alleviate the problem, we propose an efficient Selective Fusion Module (SFM), which can be plug-and-played into the advanced denoising networks to merge the deep NIR-RGB features. Specifically, we sequentially perform the global and local modulation for NIR and RGB features, and then integrate the two modulated features. Furthermore, we present a Real-world NIR-Assisted Image Denoising (Real-NAID) dataset, which covers diverse scenarios as well as various noise levels. Extensive experiments on both synthetic and our real-world datasets demonstrate that the proposed method achieves better results than state-of-the-art ones. The dataset, codes, and pre-trained models will be publicly available at https://github.com/ronjonxu/NAID.<|reference_end|>
@article{xu2024nir-assisted,
    title={NIR-Assisted Image Denoising: A Selective Fusion Approach and A
  Real-World Benchmark Dataset},
    author={Rongjian Xu, Zhilu Zhang, Renlong Wu, Wangmeng Zuo},
    journal={arXiv preprint arXiv:2404.08514},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08514},
    primaryClass={cs.CV}
}