Registration-Free Hyperspectral Reconstruction from RGB via a Permutation-Invariant Gram-Matrix Principle

[2608.14994] Registration-Free Hyperspectral Reconstruction from RGB via a Permutation-Invariant Gram-Matrix Principle Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14994 (cs) [Submitted on 15 Aug 2026] Title: Registration-Free Hyperspectral Reconstruction from RGB via a Permutation-Invariant Gram-Matrix Principle Authors: Jiangsan Zhao , Masayuki Hirafuji , Seishi Ninomiya , Jakob Geipel , Wei Guo View a PDF of the paper titled Registration-Free Hyperspectral Reconstruction from RGB via a Permutation-Invariant Gram-Matrix Principle, by Jiangsan Zhao and 4 other authors View PDF Abstract: 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. Comments: 11 pages, 10 figures, 8 tables. This work has been submitted to the IEEE for possible publication Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.14994 [cs.CV] (or arXiv:2608.14994v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14994 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jiangsan Zhao Dr. [ view email ] [v1] Sat, 15 Aug 2026 02:52:33 UTC (1,169 KB) Full-text links: Access Paper: View a PDF of the paper titled Registration-Free Hyperspectral Reconstruction from RGB via a Permutation-Invariant Gram-Matrix Principle, by Jiangsan Zhao and 4 other authors View PDF view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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