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Home
Multiscale Rgb-guided Fusion For Hyperspectral Image Super-resolution.

Home
Multiscale Rgb-guided Fusion For Hyperspectral Image Super-resolution.

Related Experiment Video

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

Multiscale RGB-Guided Fusion for Hyperspectral Image Super-Resolution.

Matteo Kolyszko¹, Marco Buzzelli¹, Simone Bianco¹

¹Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20125 Milan, Italy.

Journal of Imaging

|February 26, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

CGNet enhances hyperspectral imaging (HSI) resolution by fusing low-resolution HSI with high-resolution RGB images. This color-guided network recovers sharp spatial details while maintaining spectral accuracy, outperforming existing methods.

Keywords:

RGB guidance deep learning hyperspectral imaging image fusion super-resolution

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Area of Science:

Computer Vision
Remote Sensing
Image Processing

Background:

Hyperspectral imaging (HSI) offers detailed spectral analysis but suffers from low spatial resolution due to sensor limitations.
Existing super-resolution methods struggle to balance spatial detail recovery with spectral fidelity in HSI.

Purpose of the Study:

To introduce CGNet, a novel color-guided hyperspectral super-resolution network.
To enhance spatial resolution of HSI by effectively fusing information from RGB images.

Main Methods:

CGNet employs a dual-encoder architecture: an RGB encoder for spatial features and an HSI encoder for spectral features.
A multi-scale fusion decoder integrates features from both modalities for high-resolution HSI reconstruction.
Training utilizes a hybrid L1 and Spectral Angle Mapper (SAM) loss function.

Main Results:

CGNet achieved superior performance on ARAD1K and StereoMSI datasets at ×4 and ×6 upscaling factors.
The network demonstrated significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and reduced SAM and ΔE00.
Ablation studies validated the effectiveness of the hybrid loss function.

Conclusions:

CGNet successfully reconstructs high-resolution HSI with sharp spatial structures and preserved spectral fidelity.
The proposed method outperforms state-of-the-art baselines in hyperspectral super-resolution tasks.
CGNet offers a promising solution for applications requiring high-resolution hyperspectral data.