Removing the noise and improving the visual quality of hyperspectral images
(HSIs) is challenging in academia and industry. Great efforts have been made to
leverage local, global or spectral context information for HSI denoising.
However, existing methods still have limitations in feature interaction
exploitation among multiple scales and rich spectral structure preservation. In
view of this, we propose a novel solution to investigate the HSI denoising
using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the
complex nonlinear mapping between clean and noisy HSI. Two key components
contribute to improving the hyperspectral image denoising: A progressively
multiscale information aggregation network and a co-attention fusion module.
Specifically, we first generate a set of multiscale images and feed them into a
coarse-fusion network to exploit the contextual texture correlation.
Thereafter, a fine fusion network is followed to exchange the information
across the parallel multiscale subnetworks. Furthermore, we design a
co-attention fusion module to adaptively emphasize informative features from
different scales, and thereby enhance the discriminative learning capability
for denoising. Extensive experiments on synthetic and real HSI datasets
demonstrate that the proposed MAFNet has achieved better denoising performance
than other state-of-the-art techniques. Our codes are available at
\verb'this https URL

本文提出了一种名为 MAFNet 的多尺度自适应融合网络用于去噪高光谱图像，该网络由逐步扩展的多尺度信息聚合网络和协同注意力融合模块组成，可以有效保留丰富的光谱结构并取得比其他技术更好的去噪性能，论文代码可供参考。