Hybrid Convolutional and Attention Network for Hyperspectral Image
  Denoising
Shuai Hu Feng Gao Xiaowei Zhou Junyu Dong Qian Du
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 \url{https://github.com/summitgao/HCANet}.