# Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery

arXiv: https://arxiv.org/abs/2005.08752 · HTML: https://mlsanity.com/paper/2005.08752/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 30 of 47)

- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models](https://mlsanity.com/paper/2402.15865/) (2024)
- [DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration](https://mlsanity.com/paper/2303.06682/) (2023)
- [ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2307.14010/) (2023)
- [Deep Plug-and-Play Prior for Hyperspectral Image Restoration](https://mlsanity.com/paper/2209.08240/) (2022)
- [HSIGene: A Foundation Model For Hyperspectral Image Generation](https://mlsanity.com/paper/2409.12470/) (2024)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance](https://mlsanity.com/paper/2505.02109/) (2025)
- [Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2112.10541/) (2021)
- [Hybrid Spectral Denoising Transformer with Guided Attention](https://mlsanity.com/paper/2303.09040/) (2023)
- [Dual-Stage Approach Toward Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2204.04387/) (2022)
- [Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance](https://mlsanity.com/paper/2302.06298/) (2023)
- [Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network](https://mlsanity.com/paper/2402.17285/) (2024)
- [Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2110.11591/) (2021)
- [Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2311.17340/) (2023)
- [Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2205.14887/) (2022)
- [A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2311.08955/) (2023)
- [PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation](https://mlsanity.com/paper/2411.15922/) (2024)
- [Deep Diversity-Enhanced Feature Representation of Hyperspectral Images](https://mlsanity.com/paper/2301.06132/) (2023)
- [Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution](https://mlsanity.com/paper/2304.04589/) (2023)
- [Enhancing Unregistered Hyperspectral Image Super-Resolution via Unmixing-based Abundance Fusion Learning](https://mlsanity.com/paper/2603.07918/) (2026)
- [Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image](https://mlsanity.com/paper/2412.04201/) (2024)
- [HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution](https://mlsanity.com/paper/2501.18500/) (2025)
- [EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2409.04050/) (2024)
- [Test-time Training for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2409.08667/) (2024)
- [Hybrid Deep Learning for Hyperspectral Single Image Super-Resolution](https://mlsanity.com/paper/2510.00033/) (2025)
- [Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery](https://mlsanity.com/paper/2103.04354/) (2021)
- [Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings](https://mlsanity.com/paper/2103.10614/) (2021)
- [SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2105.10949/) (2021)
- [From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution](https://mlsanity.com/paper/2108.13584/) (2021)

## Abstract (arXiv)

Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs). However, there has been limited technical development focusing on single hyperspectral image super-resolution due to the high-dimensional and complex spectral patterns in hyperspectral image. In this paper, we make a step forward by investigating how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches for computationally efficient single hyperspectral image super-resolution, referred as SSPSR. Specifically, we introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Considering that the hyperspectral training samples are scarce and the spectral dimension of hyperspectral image data is very high, it is nontrivial to train a stable and effective deep network. Therefore, a group convolution (with shared network parameters) and progressive upsampling framework is proposed. This will not only alleviate the difficulty in feature extraction due to high-dimension of the hyperspectral data, but also make the training process more stable. To exploit the spatial and spectral prior, we design a spatial-spectral block (SSB), which consists of a spatial residual module and a spectral attention residual module. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images, and outperforms state-of-the-arts. The source code is available at urlhttps://github.com/junjun-jiang/SSPSR