Abstract
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 this https URL.
Abstract (translated)
尽管在图像去噪方面取得了显著的进展，但在消除噪声的同时恢复细粒度细节仍然具有挑战性，特别是在极低光线下。利用近红外（NIR）图像辅助可见的RGB图像去噪显示出解决这个问题的潜力，成为了一个有前景的技术。然而，现有的工作仍然很难充分利用NIR信息进行真实世界图像去噪，原因是NIR-RGB图像的内容不一致以及真实世界配对数据集的稀疏性。为了解决这个问题，我们提出了一个有效的选择性融合模块（SFM），可以将其插入到高级去噪网络中，合并深NIR-RGB特征。具体来说，我们依次对NIR和RGB特征进行全局和局部调制，然后将两个调制特征集成在一起。此外，我们还提出了一个真实世界NIR辅助图像去噪（Real-NAID）数据集，涵盖了各种场景以及各种噪声水平。对合成和真实世界数据集的广泛实验证明，与最先进的去噪方法相比，所提出的方法具有更好的效果。数据集、代码和预训练模型将公开发布在https://这个URL上。
URL
https://arxiv.org/abs/2404.08514
