The recent advancement of deep learning techniques has made great progress on
hyperspectral image super-resolution (HSI-SR). Yet the development of
unsupervised deep networks remains challenging for this task. To this end, we
propose a novel coupled unmixing network with a cross-attention mechanism,
CUCaNet for short, to enhance the spatial resolution of HSI by means of
higher-spatial-resolution multispectral image (MSI). Inspired by coupled
spectral unmixing, a two-stream convolutional autoencoder framework is taken as
backbone to jointly decompose MS and HS data into a spectrally meaningful basis
and corresponding coefficients. CUCaNet is capable of adaptively learning
spectral and spatial response functions from HS-MS correspondences by enforcing
reasonable consistency assumptions on the networks. Moreover, a cross-attention
module is devised to yield more effective spatial-spectral information transfer
in networks. Extensive experiments are conducted on three widely-used HS-MS
datasets in comparison with state-of-the-art HSI-SR models, demonstrating the
superiority of the CUCaNet in the HSI-SR application. Furthermore, the codes
and datasets will be available at:
this https URL

本研究提出了一种带有跨注意机制的耦合解混合网络 (CUCaNet)，用于通过更高空间分辨率的多光谱图像 (MSI) 增强高光谱图像 (HSI) 的空间分辨率，并在三个广泛使用的 HS-MS 数据集上进行了广泛的实验来证明其优越性。