This project aims to enhance the resolution and clarity of satellite images using Generative Adversarial Networks (GANs). High-resolution satellite imagery is crucial for applications such as environmental monitoring, urban planning, and disaster management. By leveraging the power of GANs, we aim to improve the quality of satellite images, making them more useful for these critical applications.
- Image Super-Resolution: Enhances the resolution of low-quality satellite images.
- GAN Architecture: Utilizes state-of-the-art GAN models for image generation.
- Data Preprocessing: Comprehensive preprocessing pipeline to clean, augment, and normalize satellite images.
- Model Training: Detailed model training process with hyperparameter tuning.
- Performance Metrics: Evaluation of model performance using various image quality metrics.
- Python 3.x
- TensorFlow or PyTorch
- NumPy
- OpenCV
- Matplotlib
This project is licensed under the MIT License. See the LICENSE file for more details.
- Special thanks to the open-source community for providing the tools and resources that made this project possible.
- Inspired by various research papers and projects in the field of image processing and GANs.
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