# Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion

arXiv: https://arxiv.org/abs/2201.09851 · HTML: https://mlsanity.com/paper/2201.09851/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net](https://mlsanity.com/paper/1901.03281/) (2019)
- [Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation](https://mlsanity.com/paper/1502.03121/) (2015)
- [Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network](https://mlsanity.com/paper/1706.05249/) (2017)
- [Multi-Band Image Fusion Based on Spectral Unmixing](https://mlsanity.com/paper/1603.08720/) (2016)
- [Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation](https://mlsanity.com/paper/2009.04237/) (2020)
- [Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization](https://mlsanity.com/paper/2106.07066/) (2021)

## Built on by (most cited first, 3 of 3)

- [HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models](https://mlsanity.com/paper/2306.12085/) (2023)
- [SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution](https://mlsanity.com/paper/2310.00413/) (2023)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)

## Abstract (arXiv)

To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention. This technique aims to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB image in order to obtain an HR HSI. Recently, deep learning architectures have been used to address the HSI super-resolution problem and have achieved remarkable performance. However, they ignore the degradation model even though this model has a clear physical interpretation and may contribute to improve the performance. We address this problem by proposing a method that, on the one hand, makes use of the linear degradation model in the data-fidelity term of the objective function and, on the other hand, utilizes the output of a convolutional neural network for designing a deep prior regularizer in spectral and spatial gradient domains. Experiments show the performance improvement achieved with this strategy.