# Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion

arXiv: https://arxiv.org/abs/2205.03742 · HTML: https://mlsanity.com/paper/2205.03742/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 64 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing](https://mlsanity.com/paper/1810.12000/) (2018)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Hyperspectral pansharpening: a review](https://mlsanity.com/paper/1504.04531/) (2015)
- [Material Based Object Tracking in Hyperspectral Videos: Benchmark and Algorithms](https://mlsanity.com/paper/1812.04179/) (2018)
- [Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net](https://mlsanity.com/paper/1901.03281/) (2019)
- [Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution](https://mlsanity.com/paper/2007.05230/) (2020)
- [Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/1804.05042/) (2018)
- [Guided Deep Decoder: Unsupervised Image Pair Fusion](https://mlsanity.com/paper/2007.11766/) (2020)
- [ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features](https://mlsanity.com/paper/1901.07925/) (2019)
- [Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution](https://mlsanity.com/paper/2007.14007/) (2020)
- [Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning](https://mlsanity.com/paper/2006.10300/) (2020)

## Built on by (most cited first, 18 of 18)

- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01231/) (2024)
- [Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01224/) (2024)
- [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2505.23248/) (2025)
- [Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.01095/) (2024)
- [Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01372/) (2024)
- [DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning](https://mlsanity.com/paper/2408.05523/) (2024)
- [Reduction of Class Activation Uncertainty with Background Information](https://mlsanity.com/paper/2305.03238/) (2023)
- [Physics-Inspired Degradation Models for Hyperspectral Image Fusion](https://mlsanity.com/paper/2402.02411/) (2024)
- [Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events](https://mlsanity.com/paper/2405.08251/) (2024)
- [Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models](https://mlsanity.com/paper/2501.08195/) (2025)
- [Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data](https://mlsanity.com/paper/2310.07223/) (2023)
- [Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging](https://mlsanity.com/paper/2404.02656/) (2024)
- [NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment](https://mlsanity.com/paper/2503.07004/) (2025)
- [MambaX: Image Super-Resolution with State Predictive Control](https://mlsanity.com/paper/2511.18028/) (2025)
- [Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2601.21338/) (2026)
- [Super-r'esolution non supervis'ee d'images hyperspectrales de t'el'ed'etection utilisant un entrainement enti`erement synth'etique](https://mlsanity.com/paper/2602.02552/) (2026)

## Abstract (arXiv)

Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perform the fusion task by means of multifarious pixel-level priors. Yet the intrinsic effects of a large distribution gap between HS-MS data due to differences in the spatial and spectral resolution are less investigated. The gap might be caused by unknown sensor-specific properties or highly-mixed spectral information within one pixel (due to low spatial resolution). To this end, we propose a subpixel-level HS super-resolution framework by devising a novel decoupled-and-coupled network, called DC-Net, to progressively fuse HS-MS information from the pixel- to subpixel-level, from the image- to feature-level. As the name suggests, DC-Net first decouples the input into common (or cross-sensor) and sensor-specific components to eliminate the gap between HS-MS images before further fusion, and then fully blends them by a model-guided coupled spectral unmixing (CSU) net. More significantly, we append a self-supervised learning module behind the CSU net by guaranteeing the material consistency to enhance the detailed appearances of the restored HS product. Extensive experimental results show the superiority of our method both visually and quantitatively and achieve a significant improvement in comparison with the state-of-the-arts. Furthermore, the codes and datasets will be available at https://sites.google.com/view/danfeng-hong for the sake of reproducibility.