@inproceedings{
xiao2024bridging,
title={Bridging Fourier and Spatial-Spectral Domains for Hyperspectral Image Denoising},
author={Jiahua Xiao and Yang Liu and Shizhou Zhang and Xing Wei},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=D88kAvlmQJ}
}
Remarkable progresses have been made in hyperspectral image (HSI) denoising. However, the majority of existing methods are predominantly confined to the spatial-spectral domain, overlooking the untapped potential inherent in the Fourier domain. This paper presents a novel approach to address HSI denoising by bridging the information from the Fourier and spatial-spectral domains. Our method highlights key insights into the Fourier properties within spatial and spectral domains through the Fourier transform. Specifically, we note that the amplitude predominantly encodes noise and photon reflection characteristics, while the phase holds structural information. Additionally, the Fourier transform offers a receptive field that spans the entire image, enabling effective global noise distribution capture. These insights unveil new perspectives on the physical properties of HSIs, motivating us to leverage complementary information exchange between Fourier and spatial-spectral domains. To this end, we introduce the Fourier-prior Integration Denoising Network (FIDNet), a potent yet straightforward approach that utilizes Fourier insights to synergistically interact with spatial-spectral domains for superior HSI denoising. In FIDNet, we independently extract spatial and Fourier features through dual branches and merge these representations to enhance spectral evolution modeling through the inherent structure consistency constraints and continuing reflection variation revealed in Fourier prior. Our proposed method demonstrates robust generalization across synthetic and real-world benchmark datasets, outperforming state-of-the-art methods in both quantitative quality and visual results.
Bridging Fourier and Spatial-Spectral Domains for Hyperspectral Image Denoising
Jiahua Xiao Yang Liu Shizhou Zhang Xing Wei