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 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 output, 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.

翻译：高空间分辨率高光谱图像（HSI）对于遥感与医学成像等应用至关重要，然而高光谱传感器在本质上需要以牺牲空间细节为代价来换取光谱丰富度。将高空间分辨率多光谱图像（HR-MSI）与低空间分辨率高光谱图像（LR-HSI）相融合，是一条在不牺牲光谱保真度的前提下恢复精细空间结构的可行途径。当前大多数先进的HSI-MSI融合方法均需依赖点扩散函数（PSF）标定或真实的高分辨率HSI（HR-HSI）作为地面真值，而这二者在实际场景中均难以获取。本文提出SpectraLift，一个完全自监督的融合框架，仅利用多光谱图像的光谱响应函数（SRF）即可实现LR-HSI与HR-MSI的融合。SpectraLift通过以下方式训练一个轻量级的逐像素多层感知机（MLP）网络：（i）将SRF应用于LR-HSI生成合成的低空间分辨率多光谱图像（LR-MSI）作为输入，（ii）以LR-HSI作为输出目标，（iii）以估计的LR-HSI与真实LR-HSI之间的$\ell_1$光谱重建损失作为优化目标。在推理阶段，SpectraLift利用训练好的网络将HR-MSI逐像素映射为HR-HSI的估计结果。SpectraLift可在数分钟内收敛，对空间模糊与分辨率变化具有鲁棒性，并在PSNR、SAM、SSIM和RMSE等基准指标上优于当前最先进的方法。