Ying Yangke, Wang Jin, Shi Yunhui, Ling Nam
Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China.
Beijing Institute of Artificial Intelligence, School of Computer Science, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2024 Sep 24;24(19):6184. doi: 10.3390/s24196184.
Recently, deep unfolding network methods have significantly progressed in hyperspectral snapshot compressive imaging. Many approaches directly employ Transformer models to boost the feature representation capabilities of algorithms. However, they often fall short of leveraging the full potential of self-attention mechanisms. Additionally, current methods lack adequate consideration of both intra-stage and inter-stage feature fusion, which hampers their overall performance. To tackle these challenges, we introduce a novel approach that hybridizes the sparse Transformer and wavelet fusion-based deep unfolding network for hyperspectral image (HSI) reconstruction. Our method includes the development of a spatial sparse Transformer and a spectral sparse Transformer, designed to capture spatial and spectral attention of HSI data, respectively, thus enhancing the Transformer's feature representation capabilities. Furthermore, we incorporate wavelet-based methods for both intra-stage and inter-stage feature fusion, which significantly boosts the algorithm's reconstruction performance. Extensive experiments across various datasets confirm the superiority of our proposed approach.
近年来，深度展开网络方法在高光谱快照压缩成像方面取得了显著进展。许多方法直接采用Transformer模型来提升算法的特征表示能力。然而，它们往往未能充分发挥自注意力机制的全部潜力。此外，当前方法对阶段内和阶段间特征融合的考虑不足，这阻碍了它们的整体性能。为应对这些挑战，我们引入了一种新颖的方法，该方法将稀疏Transformer和基于小波融合的深度展开网络相结合，用于高光谱图像（HSI）重建。我们的方法包括开发空间稀疏Transformer和光谱稀疏Transformer，分别用于捕捉HSI数据的空间和光谱注意力，从而增强Transformer的特征表示能力。此外，我们将基于小波的方法用于阶段内和阶段间特征融合，这显著提升了算法的重建性能。在各种数据集上进行的大量实验证实了我们提出的方法的优越性。
