Learning a Self-Supervised Low-Rank Decomposition Network for Hyperspectral Image Super-Resolution

Hyperspectral image (HSI) super-resolution, which reconstructs a high-resolution HSI (HR-HSI) through hyperspectral and multispectral image fusion (HMIF) tasks that integrate a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI), has emerged as a promising technique for enhancing spatial–spectral quality. Recently, low-rank representations have demonstrated significant advances in various hyperspectral-related applications, providing an effective solution to HMIF tasks. However, most existing methods rely on model priors to learn the low-rank representation of HSIs, which restricts their adaptability to low-rank variations across different datasets. To address this issue, this paper introduces a self-supervised low-rank decomposition network (SSLRDN) framework specifically designed for HMIF, inspired by the observation that the HR-MSI and HR-HSI of the same scene share highly similar spatial features, whereas different hyperspectral scenes exhibit variations in both spectral and spatial features. In SSLRDN, we develop a self-supervised network to adaptively learn the low-rank decomposition (spectral subspace and spatial coefficients) across different HR-HSIs, overcoming the inefficiency of conventional alternating optimization methods where factor updates fail to mutually promote each other. Given the spatial feature consistency between HR-MSI and HR-HSI, we leverage the rich spatial information from HR-MSI to guide the learning of spatial coefficients in HR-HSI. To enhance the self-supervised learning of spatial coefficient images, we further integrate an externally pre-trained denoiser to improve their estimation accuracy, effectively fusing and mutually promoting both self-supervised and pre-trained learning paradigms. Experimental results show that the proposed method achieves superior performance in both visual quality and quantitative metrics, without requiring pretraining on external datasets.