The paper introduces TSR-ITNR, a two‑stage, self‑supervised framework for hyperspectral image super‑resolution that fuses high‑resolution multispectral and low‑resolution hyperspectral data. Stage 1 refines an implicit Tucker representation using a low‑rank spatial tensor and spectral basis, enhanced by a pretrained denoiser, to capture fine spatial details and spectral correlations. Stage 2 applies parameter‑free calibration to extract complementary corrections from both observations, preserving geometry and ensuring orthogonal complementarity, leading to superior reconstruction quality demonstrated on benchmark datasets and improved downstream segmentation performance.
By Liqian Yang, Xingchi Chen, Xinfeng Gui, Xiangyong Cao, Qianxin Yi
HyperVision introduces the first ground‑based hyperspectral pre‑trained backbone, addressing challenges of varying spectral configurations, limited annotations, and dataset diversity. It employs a channel‑adaptive dynamic embedding to unify heterogeneous inputs, a multi‑source pseudo‑labeling strategy combining SAM2 spatial cues with HyperFree spectral details, and cross‑modal knowledge distillation from a pre‑trained RGB vision model. Trained on 15k images from 26 datasets, HyperVision achieves significant improvements—up to 16.3% relative gain in hyperspectral semantic segmentation, 2.1% in object tracking AUC, and 35.5% reduction in salient object detection MAE—while requiring only head‑only adaptation.
By Guanyiman Fu, Jingtao Li, Zihang Cheng, Zhuanfeng Li, Diqi Chen, Yan Xu, Xiangyu Liu, Fengchao Xiong, Jianfeng Lu, Chengrong Chen, Jun Zhou
arXiv:2604.08884v2 Announce Type: replace-cross
Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on RGB image understanding, yet their ability to use spectral evide...
By Xinyu Zhang, Zurong Mai, Qingmei Li, Xiaoya Fan, Zjin Liao, Haoyuan Liang, Yibin Wen, Yuhang Chen, Chan Tsz Ho, Bi Tianyuan, Ruifeng Su, Zihao Qiang, Juepeng Zheng, Jianxi Huang, Yutong Lu, Haohuan Fu
XCalib is an unsupervised dense registration framework that aligns thermal and visible video streams by optimizing virtual pinhole camera parameters and predicted monocular depth, thereby restricting spatial displacements to physically valid projection geometries. It introduces a novel registration paradigm using camera parameterization as an implicit regularizer, a robust Normalized Edges Correlation (NEC) metric for cross‑spectral alignment, and demonstrates superior temporal stability and alignment accuracy on public ADAS datasets compared to unconstrained dense flow baselines.
By Aurelien Godet, Gabriel Jobert, Mauro Dalla Mura
arXiv:2607. 24110v1 Announce Type: cross Abstract: Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception.
By Minchong Chen, Xiaoyun Yuan, Minyu Cao, Jianing Zhang, Jun Zhang, Shuyang Liu, Xiaokang Yang
arXiv:2608.28341v1 Announce Type: new
Abstract: Precise dense correspondence is a fundamental prerequisite for multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent...
By Eric L. Wisotzky, Jost Triller, Simon W. H\"artl, Oliver T. Bruns, Peter Eisert, Anna Hilsmann
arXiv:2608.30782v1 Announce Type: new
Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realis...
By Bingtian Qiao, Yue Shi, Yong Guo, Wenjun Zhang, Jiezhang Cao
HyperSAM is a promptable foundation model for hyperspectral remote sensing that integrates a data‑centric synthesis pipeline with a spectral adaptation architecture based on Segment Anything Model 3 (SAM3). The model generates full‑spectrum hyperspectral cubes from high‑resolution multispectral imagery using a physics‑informed abundance‑transfer generator, and employs SAM3‑derived pseudo‑masks for object‑centric supervision. With a frozen SAM3 RGB branch, a trainable hyperspectral encoder, ControlNet‑style feature injection, and a mixture‑of‑experts mask refiner, HyperSAM demonstrates strong generalization across diverse hyperspectral tasks such as classification, anomaly detection, change detection, target detection, and airborne oil‑spill mapping.
By Li Pang, Xinqiao Wu, Jing Yao, Pedram Ghamisi, Jun Zhou, Zhengchao Chen, Deyu Meng, Xiangyong Cao
The paper introduces SAGE, a unified framework for RGB‑Thermal (RGB‑T) image alignment and fusion that integrates frequency equalization, hierarchical alignment, and subband fusion. SAGE uses invertible joint encoding and source‑specific low‑frequency modulation to generate structural and gain guidance, then performs hierarchical frequency collaborative alignment to estimate global affine geometry and refine high‑frequency details. Guided subband fusion aggregates aligned frequency coefficients under propagated guidance, coordinating complementary low‑ and high‑frequency information to reconstruct the fused image via inverse wavelet transform. Experiments on real‑world and synthetic misaligned RGB‑T datasets show that SAGE achieves competitive performance in both alignment and fusion, validating the effectiveness of source‑anchored guidance for weakly registered RGB‑T images.
By Timing Li, Yiming Sun, Boan Tao, Xiyuan Gao, Haifang Cao, Pengfei Zhu
Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception. While cross-sensor misalignment caused by different optics, viewpoints, fields of view, and exposure timings hinders practical deployment.
The paper introduces OmniRSCLIP, an end‑to‑end contrastive learning framework that extends the CLIP architecture to handle heterogeneous remote sensing sensors such as SAR, multi‑spectral imaging, and hyperspectral imaging. It achieves this by employing Spectral‑Spatial Basis Decomposition to adapt arbitrary‑channel inputs without losing pretrained visual knowledge, and a spectral‑context‑aware mask‑based contrastive learning scheme to improve fine‑grained image‑text alignment. The authors also build OmniRS5M, a large‑scale image‑text corpus covering multiple sensor modalities, and demonstrate that OmniRSCLIP maintains strong RGB performance while effectively supporting these diverse remote sensing data types.
By Xiangyang Miao, Kelu Yao, Yekai Huang, Xiaogang Xu, Junxiao Xue, Minjun Shen, Chenghui Lv, Shanji Liu, Yaying Chen, Chao Li
Hyperspectral remote sensing provides dense spectral measurements that are indispensable for material-level Earth observation, yet the construction of a general-purpose hyperspectral foundation model...