标题： 光谱提升：用于自监督高光谱图像超分辨率的物理引导光谱反演网络 显示英文标题

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

摘要： 高空间分辨率的高光谱图像（HSI）在遥感和医学成像等应用中至关重要，但HSI传感器本质上会牺牲空间细节以换取光谱丰富性。 将高空间分辨率的多光谱图像（HR-MSI）与低空间分辨率的高光谱图像（LR-HSI）融合，是一种有前景的方法，可以在不牺牲光谱保真度的情况下恢复精细的空间结构。 大多数最先进的HSI-MSI融合方法需要点扩散函数（PSF）校准或真实世界中的高分辨率HSI（HR-HSI），而这两者在实际环境中都难以获得。 我们提出了SpectraLift，这是一个完全自监督的框架，仅使用MSI的光谱响应函数（SRF）来融合LR-HSI和HR-MSI输入。 SpectraLift通过（$i$）~一个通过将SRF应用于LR-HSI得到的合成低空间分辨率多光谱图像（LR-MSI）作为输入，（$ii$）~LR-HSI作为输出，以及（$iii$）~估计值与真实LR-HSI之间的$\ell_1$光谱重建损失作为优化目标，训练一个轻量级的逐像素多层感知机（MLP）网络。 在推理时，SpectraLift使用训练好的网络将HR-MSI逐像素映射为HR-HSI估计值。 SpectraLift在几分钟内收敛，对空间模糊和分辨率无关，且在PSNR、SAM、SSIM和RMSE基准测试中优于最先进的方法。 显示英文摘要

摘要： 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.