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'https://github.com/summitgao/MAFNet'.

翻译：去除噪声并提升高光谱图像（HSI）的视觉质量是学术界和工业界的一大挑战。目前已有大量研究致力于利用局部、全局或光谱上下文信息进行HSI去噪，然而现有方法在多尺度特征交互挖掘及丰富光谱结构保持方面仍存在局限。针对这一问题，我们提出了一种基于多尺度自适应融合网络（MAFNet）的新型解决方案，该网络能够学习干净与含噪HSI之间复杂的非线性映射关系。两个关键组件有助于提升高光谱图像去噪性能：渐进式多尺度信息聚合网络与协同注意力融合模块。具体而言，我们首先生成一组多尺度图像，并将其输入粗融合网络以挖掘上下文纹理相关性；随后通过细融合网络实现并行多尺度子网络间的信息交换。此外，我们设计了一个协同注意力融合模块，可自适应地突出不同尺度的信息特征，从而增强用于去噪的判别学习能力。在合成与真实HSI数据集上的大量实验表明，所提出的MAFNet相比其他先进技术取得了更优的去噪效果。我们的代码开源于\verb'https://github.com/summitgao/MAFNet'。