发表机构
Univ. Grenoble Alpes; CNRS Grenoble INP; GIPSA-Lab; Institut Universitaire de France (IUF)（格勒诺布尔阿尔卑斯大学; 法国国家科学研究中心格勒诺布尔国立理工学院; 格勒诺布尔图像、信号、声学与自动化实验室; 法国大学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对真实跨分辨率失配问题，提出一种轻量可解释的RGB引导高光谱超分辨率框架，结合跨模态流对齐与Gram-Schmidt正交化融合，在提升重建精度的同时加快了速度，支持多光谱与尺度因子且无需重训。
AI 中文摘要
紧凑型快照高光谱相机可为地面机器视觉提供丰富的瞬时光谱测量，但其空间分辨率低于标准RGB相机。RGB引导的高光谱超分辨率（HSR）通过将高分辨率RGB引导图的空间细节迁移至低分辨率高光谱图像（HSI），以解决该局限。这类双相机系统通常采用水平支架几何结构，因视场不同需进行跨相机图像配准，但残留配准误差会引入虚假高频细节。现有学习型未对齐融合方法通常针对固定光谱支持和空间尺度因子训练，且计算量大，限制了其在不同传感器间的灵活性。本文提出一种轻量且可解释的RGB引导HSR框架，将跨模态流对齐与基于模型的Gram-Schmidt正交化融合相结合。该方法首先将RGB引导图扭曲至HSI网格，再通过测量局部对齐可靠性估计基于能量的置信权重图，此权重图既用于加权最小二乘光谱回归，也用于超分辨率估计与HSI保留估计间的门控融合。与现有学习型方法不同，所提框架计算量低，且无需重新训练即可支持VIS-NIR光谱支持和尺度因子。在Real基准上的实验表明，所提方法较学习型融合基线提升了重建精度，同时速度显著更快。在通过真实RGB-HSI双相机装置采集的34帧序列上，降分辨率定量评估验证了该方法在真实跨传感器辐射、噪声和几何差异下的有效性，而原生分辨率定性结果则证明其可部署于完整的51波段VIS-NIR采集。
英文摘要
Compact snapshot hyperspectral cameras provide rich instantaneous spectral measurements for ground-level machine vision, but at lower spatial resolution than standard RGB cameras. RGB-guided hyperspectral super-resolution (HSR) addresses this limitation by transferring spatial detail from a high-resolution RGB guide to a low-resolution hyperspectral image (HSI). These dual-camera systems are typically in a horizontal rig geometry, requiring cross-camera image alignment due to different fields of view. However, residual misregistration can inject spurious high-frequency details. Existing learned unaligned-fusion methods are usually trained for a fixed spectral support and spatial scale factors and can be computationally demanding, limiting their flexibility across sensors. We propose a lightweight and interpretable RGB-guided HSR framework combining cross-modal flow alignment with model-based Gram-Schmidt orthogonalization fusion. The method first warps the RGB guide onto the HSI grid, then estimates an energy-based confidence weight map by measuring local alignment reliability. This map is then used both in a weighted least-squares spectral regression and in a gated fusion between the super-resolved estimate and an HSI-preserving estimate. Unlike existing learned methods, the proposed framework has a low computational footprint and supports VIS-NIR spectral supports and scale factors without retraining. Experiments on the Real benchmark show that the proposed method improves reconstruction accuracy over learned fusion baselines while remaining substantially faster. On a 34-frame sequence acquired with our real RGB-HSI dual-camera setup, a reduced-resolution quantitative evaluation validates the method under genuine cross-sensor radiometric, noise, and geometric differences, while native-resolution qualitative results demonstrate deployment on the full 51-band VIS-NIR acquisition.
