arXiv Computer Vision
Cross-Spectral Dense Correspondence for Multimodal Spectral Medical Imaging
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PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation
arXiv:2511. 13020v2 Announce Type: replace-cross Abstract: Although hyperspectral imaging offers unparalleled non-invasive physiological insight, its bulky hardware, slow acquisition, and regulatory burden severely limit its clinical availability.
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Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment
arXiv:2609.01060v1 Announce Type: cross Abstract: Compact snapshot hyperspectral cameras provide rich instantaneous spectral measurements for ground-level machine vision, but at lower spatial resolut...
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Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution
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.
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HyVIC: A Metric-Driven Spatio-Spectral Hyperspectral Image Compression Architecture Based on Variational Autoencoders
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arXiv AI
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JSolver: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections
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arXiv Computer Vision
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Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography
The paper introduces two lightweight spectral adapters—Directional Spectral Adapter (DiSECT) and Spectral Instance-Guided Adapter (SiGA)—to adapt the Segment Anything Model (SAM) for accurate segmentation of colorectal liver metastases in contrast‑enhanced CT scans. SiGA achieves the highest single‑point Dice score of 0.77 and performs comparably to a 3D nnU‑Net baseline under no‑prompt inference, while DiSECT requires only 0.14 million trainable parameters. The study evaluates the adapters on 446 CT volumes across various prompting regimes, demonstrating that spectral adapters can efficiently adapt SAM with limited trainable parameters while maintaining strong segmentation accuracy.