This repository contains the implementation of enhancing satellite images using Super-Resolution Generative Adversarial Network (SRGAN). You can find the Generator and Discriminator checkpoints here. For training we used RSVQA high quality images.
Super-Resolution Generative Adversarial Network (SRGAN) is a deep learning model that generates high-resolution images from low-resolution inputs. This technique is particularly useful in satellite image processing where enhancing image quality is crucial for various applications such as remote sensing, urban planning, and environmental monitoring. The methodology implemented in this repository is based on the following paper:
Title: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Authors: Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
Link: arXiv:1609.04802