Hyperspectral Imaging

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Latest papers 126

9 papers in the last four weeks, down 44% on the four weeks before. 0.1% of all new papers.

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HS-3D-NeRF: 3D Surface and Hyperspectral Reconstruction From Stationary Hyperspectral Images Using Multi-Channel NeRFs
Feb 18, 2026Kibon Ku, Talukder Z. Jubery, Adarsh Krishnamurthy +13D Reconstruction Neural Radiance Fields
Iowa State University, Ames, Iowa, USA
Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution
Jan 29, 2026Ji-Xuan He, Guohang Zhuang, Bo Junge +5Hyperspectral Image Super-Resolution Hyperspectral Imaging
School of Computer Science and Technology, Xi’an Jiaotong University · School of Computer Science and Information Engineering, Hefei University of Technology
Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction
Oct 2, 2025Yi Ai, Yuanhao Cai, Yulun Zhang +2Flow Matching Image Reconstruction
School of Computer Science, Shanghai Jiao Tong University, China · Department of Computer Science, Johns Hopkins University, USA · School of Engineering, Westlake University, China
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications
Mar 3, 2025Yuchen Xiang, Zhaolu Liu, Monica Emili Garcia-Segura +12Self-Supervised Learning Physics-Guided Image Restoration
Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK · Department of Mathematics, Imperial College London, London, UK · Department of Clinical Neurosciences and NIHR Biomedical Research Centre, University of Cambridge, Cambridge, UK +4
Filterless Snapshot Hyperspectral Imaging using Guided Patch Diffusion
Dec 3, 2024Dean Hazineh, Luca Sacchi, Davide Cassara +2Diffusion Model Guidance Physics-Guided Image Restoration
Harvard University
Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
Jun 29, 2018Nevrez Imamoglu, Yu Oishi, Xiaoqiang Zhang +4Salient Object Detection Hyperspectral Imaging
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan · School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China

Feb 18, 2026J/K move · Enter open · S save

HS-3D-NeRF: 3D Surface and Hyperspectral Reconstruction From Stationary Hyperspectral Images Using Multi-Channel NeRFs

Kibon Ku, Talukder Z. Jubery, Adarsh Krishnamurthy, Baskar Ganapathysubramanian

Iowa State University, Ames, Iowa, USA

3D Reconstruction Neural Radiance Fields Multi-View 3D Reconstruction Hyperspectral Imaging Agricultural Image Analysis

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Advances in hyperspectral imaging (HSI) and 3D reconstruction have enabled accurate, high-throughput characterization of agricultural produce quality and plant phenotypes, both essential for advancing agricultural sustainability and breeding programs. HSI captures detailed biochemical features of produce, while 3D geometric data substantially improves morphological analysis. However, integrating these two modalities at scale remains challenging, as conventional approaches involve complex hardware setups incompatible with automated phenotyping systems. Recent advances in neural radiance fields (NeRF) offer computationally efficient 3D reconstruction but typically require moving-camera setups, limiting throughput and reproducibility in standard indoor agricultural environments. To address these challenges, we introduce HSI-SC-NeRF, a stationary-camera multi-channel NeRF framework for high-throughput hyperspectral 3D reconstruction targeting postharvest inspection of agricultural produce. Multi-view hyperspectral data is captured using a stationary camera while the object rotates within a custom-built Teflon imaging chamber providing diffuse, uniform illumination. Object poses are estimated via ArUco calibration markers and transformed to the camera frame of reference through simulated pose transformations, enabling standard NeRF training on stationary-camera data. A multi-channel NeRF formulation optimizes reconstruction across all hyperspectral bands jointly using a composite spectral loss, supported by a two-stage training protocol that decouples geometric initialization from radiometric refinement. Experiments on three agricultural produce samples demonstrate high spatial reconstruction accuracy and strong spectral fidelity across the visible and near-infrared spectrum, confirming the suitability of HSI-SC-NeRF for integration into automated agricultural workflows.

Latest papers 126

HS-3D-NeRF: 3D Surface and Hyperspectral Reconstruction From Stationary Hyperspectral Images Using Multi-Channel NeRFs

Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution

Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction

Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications

Filterless Snapshot Hyperspectral Imaging using Guided Patch Diffusion

Hyperspectral Image Dataset for Benchmarking on Salient Object Detection