基于分组双阶段双向卷积长短期方法的高光谱图像超分辨率网络

Hyperspectral image super-resolution network based on grouped two-stage biconvolution long-term and short-term method

文章提出基于分组的双阶段Bi-ConvLSTM网络(GDBN),可以充分利用图像的空间和光谱信息,通过使用以波段为单位的分组策略,有效缓解了计算负担,并对光谱信息进行保护。在编码器的不同阶段,对浅层信息提取模块和深度特征提取模块进行不同层次信息的提取,浅层信息提取模块能够对不同尺度的浅层特征信息进行充分捕捉,深度特征提取模块能够捕捉图像的高频特征信息。文章还引入通道注意力机制,增强网络对特征的组织能力,并在自然数据集cave上进行大量实验,效果普遍优于目前主流的深度学习方法。

In this paper,a two-stage Bi-ConvLSTM network based on grouping(GDBN)is proposed,which can make full use of the spatial and spectral information of images,and effectively relieve the computational burden and protect the spectral information by using the grouping strategy based on band units.At different stages of the encoder,the shallow information extraction module and the depth feature extraction module can extract different levels of information.The shallow information extraction module can fully capture the shallow feature information of different scales,and the depth feature extraction module can capture the high-frequency feature information of the image.The paper also introduces channel attention mechanism to enhance the network's ability to organize features,and conducts a large number of experiments on natural data set cave,and the effect is generally better than the current mainstream deep learning methods.