Parameter-Free Spectral-Spatial Optimization Algorithm for Semiblind Hyperspectral and Multispectral Image Fusion

Jian Long, Jialin Gui, Yuanxi Peng, Yibing Zhan, Jun Li, Quan Zhang, Yulei Tian

Abstract: Semiblind fusion of hyperspectral images (HSIs) and multispectral images (MSIs) is a critical technique for generating high-resolution HSIs (HR-HSIs) without the need for complex point spread function (PSF) estimation. Despite the broad application potential of semiblind fusion algorithms, existing methods face three major challenges. First, as imaging technology advances, the spatial resolution gap between HSIs and MSIs continues to widen, making high-magnification fusion increasingly urgent. Second, especially for deep learning-based methods, existing algorithms require meticulous hyperparameter tuning to enhance fusion quality. Third, the complex fusion process impedes the speed of image fusion. To address these challenges, we propose a parameter-free spectral-spatial optimization algorithm specifically designed to handle high-magnification differences in the semiblind fusion of HSI and MSI. This method enables fast fusion using simple matrix operations and consists of three main steps: 1) rapidly computing an initial solution using the Moore-Penrose inverse of the spatial response function (SRF); 2) constructing spectral errors from the initial solution to effectively extract spectral information; and 3) reconstructing spatial details using MSI to form spatial errors, thereby accurately reconstructing HR-HSI for high-quality fusion. Comparative experiments on two simulated and three real datasets with state-of-the-art algorithms demonstrate that our proposed semiblind fusion method not only achieves $64\times $ high-magnification fusion but also reduces the computation time to just 23.9%–49.5% of that required by the fastest competing methods. The code is available at https://github.com/Long-ji/PFSSOA.