Single Hyperspectral Image Super-Resolution Utilizing Implicit Neural Representations

Bohdan Perederei, Faisal Z. Qureshi

2025

Abstract

Hyperspectral image super-resolution is a crucial task in computer vision, aiming to enhance the spatial resolution of hyperspectral data while maintaining spectral fidelity. In this paper, we introduce highlights and outcomes of our research, in which we developed, explored, and evaluated different techniques and methods based on Implicit Neural Representations (INRs) for conducting Single Hyperspectral Image Super-Resolution. Despite the potential of INRs, their application to hyperspectral image super-resolution still needs to be explored, with significant room for further investigation. Our primary goal was to adapt strategies and techniques from models originally developed for multispectral image super-resolution, especially SIREN-based INRs and the Dual Interactive Implicit Neural Network architectures. We also explored feature extraction from hyper-spectral images using a convolutional neural network autoencoder that allowed us to capture spatial-spectral patterns for further enhancement. Furthermore, as a part of the research, we validated and compared different functions, such as MSE, RMSE, MAE, PSNR, SAD, SAM, and SSIM, to evaluate their effectiveness as loss functions for training INRs.

Paper Citation

in Harvard Style

Perederei B. and Qureshi F. (2025). Single Hyperspectral Image Super-Resolution Utilizing Implicit Neural Representations. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 628-635. DOI: 10.5220/0013153900003905

in Bibtex Style

@conference{icpram25,
author={Bohdan Perederei and Faisal Qureshi},
title={Single Hyperspectral Image Super-Resolution Utilizing Implicit Neural Representations},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={628-635},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013153900003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Single Hyperspectral Image Super-Resolution Utilizing Implicit Neural Representations
SN - 978-989-758-730-6
AU - Perederei B.
AU - Qureshi F.
PY - 2025
SP - 628
EP - 635
DO - 10.5220/0013153900003905
PB - SciTePress