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arXiv · 2404.08514

NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset

Rongjian Xu·Zhilu Zhang·Renlong Wu·Wangmeng Zuo
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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 https://github.com/ronjonxu/NAID.

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Rongjian Xu, Zhilu Zhang, Renlong Wu, Wangmeng Zuo. 2024-04-12. NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset. https://doi.org/10.1109/tmm.2024.3521833

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