ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images

TL;DR

ResSR is a fast, model-based method for multispectral image super-resolution that achieves high-quality results without supervised training, by decoupling spectral and spatial processing into two efficient steps.

Contribution

ResSR introduces a novel, computationally efficient MSI super-resolution approach that avoids supervised learning and spatially-coupled optimization, enabling faster processing with comparable or better quality.

Findings

ResSR is 2 to 10 times faster than existing methods.

ResSR achieves comparable or improved reconstruction quality.

ResSR does not require supervised training or spatially-coupled optimization.

Abstract

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$\times$ to 10$\times$ faster. Code is available at https://github.com/hdsullivan/ResSR.