@inproceedings{
wang2024rffnet,
title={{RFFN}et: Towards Robust and Flexible Fusion for Low-Light Image Denoising},
author={Qiang Wang and Yuning Cui and Yawen Li and paulruan and zhuben and Wenqi Ren},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=w6rJxukAap}
}
Low-light environments will introduce high-intensity noise into images. Containing fine details with reduced noise, near-infrared/flash images can serve as guidance to facilitate noise removal. 
However, existing fusion-based methods fail to effectively suppress artifacts caused by inconsistency between guidance/noisy image pairs and do not fully excavate the useful information contained in guidance images. In this paper, we propose a robust and flexible fusion network (RFFNet) for low-light image denoising. Specifically, we present a multi-scale inconsistency calibration module to address inconsistency before fusion by first mapping the guidance features to multi-scale spaces and calibrating them with the aid of pre-denoising features in a coarse-to-fine manner. Furthermore, we develop a dual-domain adaptive fusion module to adaptively extract useful high-/low-frequency signals from the guidance features and then highlight the informative frequencies.
Extensive experimental results demonstrate that our method achieves state-of-the-art performance on NIR-guided RGB image denoising and flash-guided no-flash image denoising.
RFFNet: Towards Robust and Flexible Fusion for Low-Light Image Denoising
Qiang Wang Yuning Cui Yawen Li paulruan zhuben Wenqi Ren