Hyperspectral and Multispectral Image Fusion via Logarithmic Low-Rank Tensor Ring Decomposition

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 11583 - 11597
• Main Authors: Zhang, Jun, Zhu, Lipeng, Deng, Chengzhi, Li, Shutao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer vision; Correlation; Decomposition; Hyperspectral image fusion; Hyperspectral imaging; Image acquisition; Image fusion; logarithmic function; Logarithms; Matrix decomposition; Minimization; proximal alternating minimization; Regularization; Spectral correlation; tensor nuclear norm (TNN); tensor ring (TR) decomposition; Tensors
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3416335

Integrating a low-spatial-resolution hyperspectral image with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based method has received growing attention owing to its superior performance in preserving the spatial-spectral correlation. Based on the TR decomposition, the degradation model is developed via the spectral and spatial cores in TR. Here, we study the low-rankness of TR factors from the TNN perspective and consider the mode-2 logarithmic TNN (LTNN) on each TR factor. A novel fusion model is proposed by incorporating this LTNN regularization and the weighted total variation which is to promote the continuity of HR-HSI in the spatial-spectral domain. Meanwhile, we have devised a proximal alternating minimization algorithm to solve the proposed model. The experimental results indicate that our method improves the visual quality and exceeds the existing state-of-the-art fusion approaches concerning various quantitative metrics.