AI 中文总结
该研究提出基于置换不变Gram矩阵原理的方法，无需配准、已知相机响应函数或配对监督，可从RGB直接重建高光谱图像，性能与需严格假设的方法相当且鲁棒性强。
AI 中文摘要
从低分辨率高光谱图像（LR-HSI）和高分辨率RGB图像（HR-RGB）重建空间与光谱高分辨率高光谱图像（HR-HSI），通常需假设精确配准和已知相机响应函数（CRF），但这两个假设在不同传感器场景中难以满足。本文通过置换不变监督原理消除这两个假设：解混丰度图的Gram矩阵依赖于共享材料组成，与像素顺序无关；匹配丰度Gram矩阵可实现无需空间对应和预定义CRF的RGB到HSI映射学习。在HR-RGB像素完全随机置换下，现有最优融合方法会失效，而本文方法在逆重新索引用于评估后仍保持不变。基于该原理，残差光谱超分辨率函数可直接将HR-RGB映射为HR-HSI，无需配准、已知CRF或配对监督。在室内、自然场景和遥感基准测试中，该方法精度与需上述假设的方法相当，且在假设被违反时仍具鲁棒性；损失消融实验进一步表明，重建精度对匹配Gram矩阵所用的特定差异不敏感，说明性能主要源于置换不变原理而非损失调优。
英文摘要
Reconstructing a spatially and spectrally high-resolution hyperspectral image (HR-HSI) from a low-resolution HSI (LR-HSI) and a high-resolution RGB image (HR-RGB) usually assumes precise registration and a known camera response function (CRF). Both assumptions are difficult to satisfy with different sensors. We remove both through a permutation-invariant supervision principle: the Gram matrix of an unmixed abundance map depends on shared material composition but not on pixel ordering. Matching abundance Gram matrices therefore allows RGB-to-HSI mapping to be learned without spatial correspondence and without a predefined CRF. Under a full random permutation of HR-RGB pixels, a state-of-the-art fusion method collapses, whereas our reconstruction is unchanged after inverse reindexing for evaluation. Building on this principle, a residual spectral super-resolution function maps HR-RGB directly to HR-HSI without registration, known CRF, or paired supervision. Across indoor, natural-scene, and remote-sensing benchmarks, the method achieves accuracy comparable to approaches that require these assumptions while remaining robust when they are violated. Loss ablations further show that reconstruction accuracy is largely insensitive to the specific discrepancy used to match the Gram matrices, indicating that performance arises primarily from the permutation-invariant principle rather than loss tuning.
Comments11 pages, 10 figures, 8 tables. This work has been submitted to the IEEE for possible publication
