Joint Nonlocal, Spectral, and Similarity Low-Rank Priors for Hyperspectral-Multispectral Image Fusion

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.

Automated summary

The study presents a method for fusing high-spatial-and-low-spectral resolution multispectral image (MSI) with a low-spatial-and-high-spectral resolution hyperspectral image (HSI) to generate a high-resolution image (HRI). The proposed method uses various low-rank regularizations jointly and incorporates a nonlocal patch-based denoiser in the alternating direction method of multipliers (ADMM) to refine the spatial and spectral correlations of the HRI from the individual HSI and MSI data. The method outperforms state-of-the-art methods in recovering low-contrast areas and introduces a rank-one similarity prior, which is found to be an inherent characteristic of the HRI.