论文标题
偶联的毫用网的跨注意力，用于无监督的高光超分辨率
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
论文作者
论文摘要
深度学习技术的最新进步在高光谱图像超分辨率（HSI-SR）上取得了巨大进步。然而，无监督的深层网络的发展仍然具有挑战性。为此，我们提出了一个新型的耦合的Untriging网络，具有交叉意见机制，即简称Cucanet，以通过高空间分辨率的多光谱图像（MSI）来增强HSI的空间分辨率。受耦合光谱脉冲的启发，两潮卷积自动编码器框架被视为将MS和HS数据共同分解为频谱有意义的基础和相应系数的骨架。 Cucanet能够通过在网络上执行合理的一致性假设来自适应地学习光谱和空间响应函数。此外，设计了一个交叉意见模块，以在网络中产生更有效的空间光谱信息传输。与最先进的HSI-SR模型相比，在三个广泛使用的HS-MS数据集上进行了广泛的实验，证明了Cucanet在HSI-SR应用中的优越性。此外，这些代码和数据集将在以下网址提供：https：//github.com/danfenghong/eccv2020_cucanet。
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: https://github.com/danfenghong/ECCV2020_CUCaNet.
