# Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation

arXiv: https://arxiv.org/abs/2009.04237 · HTML: https://mlsanity.com/paper/2009.04237/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2312.01061/) (2023)
- [Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors](https://mlsanity.com/paper/2211.15307/) (2022)
- [Hyperspectral and Multispectral Image Fusion Using the Conditional Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2307.03423/) (2023)
- [Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion](https://mlsanity.com/paper/2201.09851/) (2022)
- [Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems](https://mlsanity.com/paper/2306.17197/) (2023)
- [Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency](https://mlsanity.com/paper/2307.03413/) (2023)
- [Hyperspectral Image Generation with Unmixing Guided Diffusion Model](https://mlsanity.com/paper/2506.02601/) (2025)
- [THAT: Token-wise High-frequency Augmentation Transformer for Hyperspectral Pansharpening](https://mlsanity.com/paper/2508.08183/) (2025)
- [Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2601.21338/) (2026)

## Abstract (arXiv)

Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-resolution (LR) HSI and a high-resolution (HR) conventional image of the same scene to obtain an HR HSI. In this work, we propose a method that integrates a physical model and deep prior information. Specifically, a novel, yet effective two-stream fusion network is designed to serve as a {regularizer} for the fusion problem. This fusion problem is formulated as an optimization problem whose solution can be obtained by solving a Sylvester equation. Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and {the} learned prior to reconstruct the final HR HSI. Experimental results on {both simulated and real data} demonstrate the superiority of the proposed method over other state-of-the-art methods on both quantitative and qualitative comparisons.