{"id":24461378,"url":"https://github.com/rushikeshghuge-19/sar_colorization","last_synced_at":"2026-05-18T07:32:59.767Z","repository":{"id":271748410,"uuid":"914448894","full_name":"RushikeshGhuge-19/SAR_Colorization","owner":"RushikeshGhuge-19","description":"This project aims to enhance Synthetic Aperture Radar (SAR) imagery by developing a deep learning-based system that colorizes grayscale SAR images and extracts meaningful features. 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The solution integrates advanced feature extraction and natural language processing (NLP) for prompt-based user interactions, similar to ChatGPT.\n\nFeatures Implemented\n- SAR Image Colorization: Utilized a Conditional Generative Adversarial Network (cGAN) to generate colorized versions of SAR images.\n- Feature Extraction: Leveraged convolutional neural networks (CNNs) to identify and extract key features from SAR images.\n- Prompt Analysis: Implemented basic NLP capabilities to analyze user prompts for targeted image analysis.\n- Data Subsetting: Created subsets of large datasets for efficient model training and testing.\n- Performance Metrics: Integrated evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to measure the quality of colorized images.\n\nTechnologies Used\n- Python\n- PyTorch: For building and training the deep learning models.\n- Torchvision: For pre-trained models and image transformations.\n- Matplotlib: For visualizing and saving generated images.\n- NumPy: For array manipulations and numerical operations.\n- scikit-image: For calculating PSNR and SSIM metrics.\n- Git: For version control and project management.\n\nHow to Run\n1. Clone the repository:\n   git clone https://github.com/RushikeshGhuge-19/SAR_colorization.git\n2. Navigate to the project directory:\n   cd SAR_colorization\n3. Install required dependencies:\n   pip install -r requirements.txt\n4. Run the main.py script to start the training and image generation:\n   python main.py\n\nFuture Enhancements\n- Fine-tune the prompt analysis using advanced NLP models.\n- Implement additional data augmentation techniques to improve model generalization.\n- Optimize model training with hyperparameter tuning.\n\nLicense\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\nAcknowledgements\nSpecial thanks to the open-source community for providing the libraries and tools that made this project possible.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frushikeshghuge-19%2Fsar_colorization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frushikeshghuge-19%2Fsar_colorization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frushikeshghuge-19%2Fsar_colorization/lists"}