Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent resolution, which limits downstream analysis. MSI super-resolution (MSI-SR) methods address this limitation by reconstructing all bands at a common high spatial resolution. Existing methods can achieve high reconstruction quality but often rely on spatially-coupled optimization or large learning-based models, leading to significant computational cost and limiting their use in large-scale or time-critical settings. In this paper, we introduce ResSR, a computationally efficient, model-based MSI-SR method that achieves high-quality reconstruction without supervised training or spatially-coupled optimization. Notably, ResSR decouples spectral and spatial processing into two sequential steps. ResSR first computes a spectrally-informed high-resolution estimate of the MSI using singular value decomposition together with a spatially-decoupled approximate forward model. It then applies a residual correction step to restore low-frequency spatial consistency while preserving high-frequency detail recovered by the spectral reconstruction. ResSR achieves comparable or improved reconstruction quality relative to existing MSI-SR methods while being 2× to 10× faster. Code is available at https://github.com/hdsullivan/ResSR.
@article{arxiv.2408.13225,
title = {ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images},
author = {Haley Duba-Sullivan and Emma J. Reid and Sophie Voisin and Charles A. Bouman and Gregery T. Buzzard},
journal= {arXiv preprint arXiv:2408.13225},
year = {2026}
}