ABSTRACT Hyperspectral image (HSI) and multispectral image (MSI) fusion aiming to improve HSI spatial resolution has attracted increasing research interests in the recent decade. Fusing hyperspectral and multispectral images is formulated as a linear inverse problem (LIP), which solution is considered to live in a lower dimensional subspace spanned by the high-resolution HSI vectors. LIP is generally ill-posed and does not have a unique solution. Therefore, some prior information or regularization terms are required to convert it to a well-posed LIP. In this paper, the smooth graph signal modelling is suggested to incorporate the spatio-spectral joint structures of the HSIs. As spatially near pixels have been expected to have similar spectral responses, the image graph model is considered to be smooth if strongly connected nodes have similar values. We suggest the consistency of subspace projection fractions corresponding to the nearby nodes by exploiting the graph Laplacian matrix and investigate an optimization algorithm based on the alternating direction method of multipliers (ADMM). Experiments on the real well-known datasets demonstrate the superiority of the proposed method compared with the current state-of-the-art HSI and MSI fusion approaches.

中文翻译：

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使用平滑图信号建模融合高光谱和多光谱图像

摘要 近年来，旨在提高 HSI 空间分辨率的高光谱图像 (HSI) 和多光谱图像 (MSI) 融合引起了越来越多的研究兴趣。融合高光谱和多光谱图像被公式化为线性逆问题 (LIP)，该解决方案被认为存在于由高分辨率 HSI 向量跨越的低维子空间中。LIP 通常是不适定的，并且没有唯一的解决方案。因此，需要一些先验信息或正则化项才能将其转换为适定的 LIP。在本文中，建议使用平滑图信号建模来结合 HSI 的空间-光谱联合结构。由于预计空间附近的像素具有相似的光谱响应，如果强连接节点具有相似的值，则图像图模型被认为是平滑的。我们通过利用图拉普拉斯矩阵建议与附近节点对应的子空间投影分数的一致性，并研究基于乘法器交替方向法（ADMM）的优化算法。与当前最先进的 HSI 和 MSI 融合方法相比，对真实知名数据集的实验证明了所提出的方法的优越性。