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

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.