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

TL;DR

This paper introduces a novel selective fusion module for NIR-assisted image denoising, effectively leveraging NIR information to improve denoising performance in real-world, low-light conditions, supported by a new diverse benchmark dataset.

Contribution

It proposes a plug-and-play selective fusion module for NIR-RGB features and introduces a comprehensive real-world NIR-assisted denoising dataset.

Findings

Outperforms state-of-the-art denoising methods on synthetic and real-world datasets.

Demonstrates effectiveness of NIR information in preserving details in low-light denoising.

Provides a publicly available dataset for future research in real-world NIR-assisted denoising.

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.