Hyperspectral image (HSI) denoising is critical for the effective analysis
and interpretation of hyperspectral data. However, simultaneously modeling
global and local features is rarely explored to enhance HSI denoising. In this
letter, we propose a hybrid convolution and attention network (HCANet), which
leverages both the strengths of convolution neural networks (CNNs) and
Transformers. To enhance the modeling of both global and local features, we
have devised a convolution and attention fusion module aimed at capturing
long-range dependencies and neighborhood spectral correlations. Furthermore, to
improve multi-scale information aggregation, we design a multi-scale
feed-forward network to enhance denoising performance by extracting features at
different scales. Experimental results on mainstream HSI datasets demonstrate
the rationality and effectiveness of the proposed HCANet. The proposed model is
effective in removing various types of complex noise. Our codes are available
at https://github.com/summitgao/HCANet.

提出了一种混合卷积和注意力网络（HCANet），通过同时建模全局和局部特征，利用卷积神经网络（CNN）和 Transformer 的优势，设计了卷积和注意力融合模块来捕捉长距离依赖和邻域光谱相关性，以及设计了多尺度前向网络来提取不同尺度的特征，从而提高多尺度信息聚合的性能，实验结果表明了 HCANet 模型的合理性和有效性。