Hyperspectral image super-resolution network based on grouped two-stage biconvolution long-term and short-term method
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.
万方数据
维普
