# A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution

arXiv: https://arxiv.org/abs/2111.08685 · HTML: https://mlsanity.com/paper/2111.08685/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 65 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 30 of 31)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018)
- [Optimizing the Latent Space of Generative Networks](https://mlsanity.com/paper/1707.05776/) (2017)
- [The 2018 PIRM Challenge on Perceptual Image Super-resolution](https://mlsanity.com/paper/1809.07517/) (2018)
- [Learning a No-Reference Quality Metric for Single-Image Super-Resolution](https://mlsanity.com/paper/1612.05890/) (2016)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network](https://mlsanity.com/paper/1803.04271/) (2018)
- [GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks](https://mlsanity.com/paper/1901.09165/) (2019)
- [Spectral Regularization for Combating Mode Collapse in GANs](https://mlsanity.com/paper/1908.10999/) (2019)
- [Attention-GAN for Object Transfiguration in Wild Images](https://mlsanity.com/paper/1803.06798/) (2018)
- [SROBB: Targeted Perceptual Loss for Single Image Super-Resolution](https://mlsanity.com/paper/1908.07222/) (2019)
- [Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities](https://mlsanity.com/paper/1701.06264/) (2017)
- [Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution](https://mlsanity.com/paper/2007.14007/) (2020)
- [Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network](https://mlsanity.com/paper/1706.05249/) (2017)
- [Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier](https://mlsanity.com/paper/1802.04881/) (2018)
- [Multisource and Multitemporal Data Fusion in Remote Sensing](https://mlsanity.com/paper/1812.08287/) (2018)
- [Adversarial nets with perceptual losses for text-to-image synthesis](https://mlsanity.com/paper/1708.09321/) (2017)
- [PoseGAN: A Pose-to-Image Translation Framework for Camera Localization](https://mlsanity.com/paper/2006.12712/) (2020)
- [Aerial Spectral Super-Resolution using Conditional Adversarial Networks](https://mlsanity.com/paper/1712.08690/) (2017)
- [Bi-GANs-ST for Perceptual Image Super-resolution](https://mlsanity.com/paper/1811.00367/) (2018)

## Built on by (most cited first, 14 of 14)

- [HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models](https://mlsanity.com/paper/2306.12085/) (2023)
- [Super-NeRF: View-consistent Detail Generation for NeRF super-resolution](https://mlsanity.com/paper/2304.13518/) (2023)
- [Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities](https://mlsanity.com/paper/2402.18062/) (2024)
- [Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review](https://mlsanity.com/paper/2304.13880/) (2023)
- [Generative Adversarial Networks for Image Super-Resolution: A Survey](https://mlsanity.com/paper/2204.13620/) (2022)
- [SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation](https://mlsanity.com/paper/2405.18224/) (2024)
- [Super-resolution Reconstruction of Single Image for Latent features](https://mlsanity.com/paper/2211.12845/) (2022)
- [A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics](https://mlsanity.com/paper/2307.05361/) (2023)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [Deep Learning Meets Process-Based Models: A Hybrid Approach to Agricultural Challenges](https://mlsanity.com/paper/2504.16141/) (2025)
- [A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution](https://mlsanity.com/paper/2309.13940/) (2023)
- [Hyperspectral Unmixing of Agricultural Images taken from UAV Using Adapted U-Net Architecture](https://mlsanity.com/paper/2409.19701/) (2024)
- [Consistency-Regularized GAN for Few-Shot SAR Target Recognition](https://mlsanity.com/paper/2601.15681/) (2026)
- [A Variational Latent-Space Framework for Uncertainty-Aware Spectral Image Emulation](https://mlsanity.com/paper/2603.21911/) (2026)

## Abstract (arXiv)

Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to generate a high-resolution (HR) HSI with higher spectral and spatial fidelity from its low-resolution (LR) counterpart. The generative adversarial network (GAN) has proven to be an effective deep learning framework for image super-resolution. However, the optimisation process of existing GAN-based models frequently suffers from the problem of mode collapse, leading to the limited capacity of spectral-spatial invariant reconstruction. This may cause the spectral-spatial distortion on the generated HSI, especially with a large upscaling factor. To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can map the generated spectral-spatial features from the image space to the latent space and produce a coupling component to regularise the generated samples. Essentially, we treat an HSI as a high-dimensional manifold embedded in a latent space. Thus, the optimisation of GAN models is converted to the problem of learning the distributions of high-resolution HSI samples in the latent space, making the distributions of the generated super-resolution HSIs closer to those of their original high-resolution counterparts. We have conducted experimental evaluations on the model performance of super-resolution and its capability in alleviating mode collapse. The proposed approach has been tested and validated based on two real HSI datasets with different sensors (i.e. AVIRIS and UHD-185) for various upscaling factors and added noise levels, and compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR, BAGAN, SR- GAN, WGAN).