Unsupervised Generative Network for Blind Hyperspectral Image Super-Resolution
Unsupervised Generative Network for Blind Hyperspectral Image Super-Resolution
复制标题
用于盲超光谱图像超分辨率的无监督生成网络
DOI：
10.1109/icip46576.2022.9897424
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Zhe Liu;X. Han;Jiande Sun;Yenwei Chen
中科院分区：
文献类型：
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作者：
Zhe Liu;X. Han;Jiande Sun;Yenwei Chen
高光谱(HS)成像在捕捉场景每个空间位置的详细光谱特征时牺牲空间分辨率以确保高光谱分辨率。为了弥补这一不足，将低分辨率HS(LR-HS)图像与高分辨率RGB(HR-RGB)图像进行融合以获得高分辨率HS(HR-HS)图像引起了人们的广泛关注。近年来，基于深度学习的全监督融合方法已被证明在高光谱图像超分辨率(HSI-SR)任务中取得了很大进展。然而，这些方法需要收集大量的训练样本并构建非盲预测模型来超分辨在受控成像条件下捕获的观测值。该研究提出了一种新的无监督生成网络(UGN)，该网络仅使用观察到的LR-HS、HR-RGB学习网络参数，而不使用相应的地面事实，并设计了空间和频谱退化块来自动学习图像退化操作，以构建端到端的盲HSI SR框架。为了验证该方法的有效性，我们在两个基准HS图像数据集上进行了实验，并与监督和无监督的盲/非盲SOTA方法进行了比较，结果表明该方法具有更好的性能。
Hyperspectral (HS) imaging sacrifices spatial resolution to ensure a high spectral resolution when capturing the detailed spectral signature at each spatial location of the scene. To compensate for this deficiency, fusing low-resolution HS (LR-HS) images with high-resolution RGB (HR-RGB) images to obtain high-resolution HS (HR-HS) images has attracted remarkable attention. Recently, deep learning-based fusion methods in a fully-supervised manner have been proven to make great progress in hyperspectral image super-resolution (HSI-SR) tasks. However, these methods require collecting a large number of training samples and constructing a non-blind prediction model to super-resolve the observations captured under controlled imaging conditions. This study proposes a novel unsupervised generative network (UGN) for learning network parameters using the observed LR-HS, HR-RGB only without the corresponding ground-truth, and designs the spatial and spectral degradation blocks to automatically learn the image degradation operations for constructing an end-to-end blind HSI SR framework. To verify the effectiveness of our proposed method, we con-duct experiments on two benchmark HS image datasets and demonstrate superior performance compared with the super-vised and unsupervised blind/non-blind SoTA methods.