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.

翻译：尽管图像去噪取得了显著进展，但在去除噪声的同时恢复精细细节仍然具有挑战性，尤其是在极低光环境下。利用近红外（NIR）图像辅助可见光RGB图像去噪显示出解决该问题的潜力，成为一种前景广阔的技术。然而，由于近红外与RGB图像之间的内容不一致性以及真实世界配对数据集的稀缺性，现有方法仍难以有效利用近红外信息进行真实图像去噪。为解决这一问题，我们提出了一种高效的选择性融合模块（SFM），该模块可即插即用于先进去噪网络，以融合深度近红外与RGB特征。具体而言，我们依次对近红外和RGB特征进行全局与局部调制，然后将两个调制后的特征进行集成。此外，我们提出了一个真实世界近红外辅助图像去噪（Real-NAID）数据集，涵盖多种场景及不同噪声水平。在合成数据集和真实世界数据集上的大量实验表明，所提方法优于现有最先进方法。该数据集、代码及预训练模型将在 https://github.com/ronjonxu/NAID 公开提供。