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

## TL;DR

This paper introduces CGNet, a new method that improves the spatial resolution of hyperspectral images using high-resolution RGB images while maintaining spectral accuracy.

## Contribution

The novel contribution is a dual-encoder network with a multi-scale fusion decoder and a hybrid loss function for hyperspectral image super-resolution.

## Key findings

- CGNet outperforms existing methods on the ARAD1K and StereoMSI benchmarks in terms of PSNR and SSIM.
- The hybrid loss combining L1 and SAM is shown to be the most effective formulation through ablation studies.
- CGNet achieves lower spectral angle mapper (SAM) and ΔE00 values, indicating better spectral fidelity and color accuracy.

## Abstract

Hyperspectral imaging (HSI) enables fine spectral analysis but is often limited by low spatial resolution due to sensor constraints. To address this, we propose CGNet, a color-guided hyperspectral super-resolution network that leverages complementary information from low-resolution hyperspectral inputs and high-resolution RGB images. CGNet adopts a dual-encoder design: the RGB encoder extracts hierarchical spatial features, while the HSI encoder progressively upsamples spectral features. A multi-scale fusion decoder then combines both modalities in a coarse-to-fine manner to reconstruct the high-resolution HSI. Training is driven by a hybrid loss that balances L1 and Spectral Angle Mapper (SAM), which ablation studies confirm as the most effective formulation. Experiments on two benchmarks, ARAD1K and StereoMSI, at ×4 and ×6 upscaling factors demonstrate that CGNet consistently outperforms state-of-the-art baselines. CGNet achieves higher PSNR and SSIM, lower SAM, and reduced ΔE00, confirming its ability to recover sharp spatial structures while preserving spectral fidelity.