The fusion of a low-spatial-and-high-spectral resolution hyperspectral image (HSI) with a high-spatial-and-low-spectral resolution multispectral image (MSI) allows synthesizing a high-resolution image (HRI), supporting remote sensing applications, such as disaster management, material identification, and precision agriculture. Unlike existing variational methods using low-rank regularizations separately, we present an HSI-MSI fusion method promoting various low-rank regularizations jointly. Our method refines the HRI spatial and spectral correlations from the individual HSI and MSI data through the proper plug-and-play (PnP) of a nonlocal patch-based denoiser in the alternating direction method of multipliers (ADMM). Notably, we consider the nonlocal self-similarity, the spectral low-rank, and introduce a rank-one similarity prior. Furthermore, we demonstrate via an extensive empirical study that the rank-one similarity prior is an inherent characteristic of the HRI. Simulations over standard benchmark datasets show the effectiveness of the proposed HSI-MSI fusion outperforming state-of-the-art methods, particularly in recovering low-contrast areas.

中文翻译：

---

用于高光谱-多光谱图像融合的联合非局部、光谱和相似性低秩先验

低空间和高光谱分辨率高光谱图像 (HSI) 与高空间和低光谱分辨率多光谱图像 (MSI) 的融合允许合成高分辨率图像 (HRI)，支持遥感应用，例如灾害管理、材料识别和精准农业。与现有的单独使用低秩正则化的变分方法不同，我们提出了一种 HSI-MSI 融合方法，共同促进各种低秩正则化。我们的方法通过乘法器交替方向方法 (ADMM) 中基于非局部补丁的降噪器的适当即插即用 (PnP) 来细化单个 HSI 和 MSI 数据的 HRI 空间和光谱相关性。值得注意的是，我们考虑了非局部自相似性、谱低秩，并在先验中引入了秩一相似性。此外，我们通过广泛的实证研究证明，秩一相似性先验是 HRI 的固有特征。对标准基准数据集的模拟表明，所提出的 HSI-MSI 融合的有效性优于最先进的方法，特别是在恢复低对比度区域方面。