Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising
This addresses denoising for hyperspectral image analysis, but it appears incremental as it combines existing CNN and Transformer approaches.
The paper tackled hyperspectral image denoising by proposing a hybrid convolutional and attention network (HCANet) to model global and local features, resulting in effective noise removal as demonstrated on mainstream datasets.
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}.