SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution

High-spatial-resolution Hyperspectral Images (HSI) are essential for applications such as remote sensing and medical imaging, yet HSI sensors inherently trade spatial detail for spectral richness. Fusing High-spatial-Resolution Multispectral Images (HR-MSI) with Low-spatial-Resolution Hyperspectral Images (LR-HSI) is a promising route to recover fine spatial structures without sacrificing spectral fidelity. Most state of the art methods for HSI-MSI fusion demand Point Spread Function (PSF) calibration or ground truth High-spatial-Resolution HSI (HR-HSI), both of which are impractical to obtain in real world settings. We present SpectraLift, a fully self-supervised framework that fuses LR-HSI and HR-MSI inputs using only the MSI's Spectral Response Function (SRF). SpectraLift trains a lightweight per-pixel Multi-Layer Perceptron (MLP) network using (i) a synthetic Low-spatial-Resolution Multispectral Image (LR-MSI) obtained by applying the SRF to the LR-HSI as input, (ii) the LR-HSI as the target, and (iii) an $\ell_{1}$ spectral reconstruction loss between the estimated and true LR-HSI as the optimization objective. At inference, SpectraLift uses the trained network to map the HR-MSI pixel-wise into a HR-HSI estimate. SpectraLift converges in minutes, is agnostic to spatial blur and resolution, and outperforms state-of-the-art methods on PSNR, SAM, SSIM, and RMSE benchmarks. An extended version of this paper providing additional experiments, metrics, further discussion, and an ablation study is available [1]. Code for SpectraLift's implementation can be obtained at https://github.com/ritikgshah/SpectraLift