Hyperspectral image (HSI) denoising is an essential procedure for HSI applications. Unfortunately, the existing Transformer-based methods mainly focus on non-local modeling, neglecting the importance of locality in image denoising. Moreover, deep learning methods employ complex spectral learning mechanisms, thus introducing large computation costs. To address these problems, we propose a hybrid spatial-spectral denoising network (HSSD), in which we design a novel hybrid dual-path network inspired by CNN and Transformer characteristics, leading to capturing both local and non-local spatial details while suppressing noise efficiently. Furthermore, to reduce computational complexity, we adopt a simple but effective decoupling strategy that disentangles the learning of space and spectral channels, where multilayer perception with few parameters is utilized to learn the global correlations among spectra. The synthetic and real experiments demonstrate that our proposed method outperforms state-of-the-art methods on spatial and spectral reconstruction. The code and details are available on https://github.com/HLImg/HSSD.

提出了一种混合的空间-光谱去噪网络(HSSD)，该网络通过设计一个新颖的CNN和Transformer特性的混合双路径网络，以高效地捕获本地和非本地空间细节，同时抑制噪声，从而解决了现有Transformer-based方法主要关注非本地建模并忽视图像去噪中局部性的问题，并采用简单但有效的分离策略降低了计算复杂性，通过使用参数较少的多层感知机学习光谱之间的全局相关性。综合和实际实验表明，我们提出的方法在空间和光谱重建方面优于最先进的方法。

光谱空间混合神经网络用于高光谱图像降噪