| --- |
| tags: |
| - super-resolution |
| - image-enhancement |
| - pytorch |
| - deep-learning |
| datasets: |
| - DIV2K |
| - Set5 |
| license: mit |
| model-index: |
| - name: EDSR |
| results: |
| - task: |
| name: Image Super-Resolution |
| type: image-super-resolution |
| dataset: |
| name: DIV2K |
| type: DIV2K |
| metrics: |
| - name: PSNR |
| type: psnr |
| value: 30.50 |
| - name: SSIM |
| type: ssim |
| value: 0.85 |
| --- |
| |
| # Super Resolution Model Comparison |
|
|
| 🚀 **Super Resolution Model Comparison** is a web application built using **Streamlit** that allows users to upload a low-resolution image and compare its enhancement using various super-resolution models (SRCNN, VDSR, and EDSR). The application provides image quality metrics (PSNR and SSIM) and processing time for each model to assess their performance. |
|
|
| --- |
|
|
| ## Features |
|
|
| - **Image Upload**: Upload low-resolution images in PNG, JPG, or JPEG formats. |
| - **Model Comparison**: Enhance the uploaded image using three popular super-resolution models: |
| - **SRCNN**: Super-Resolution Convolutional Neural Network. |
| - **VDSR**: Very Deep Super-Resolution Network. |
| - **EDSR**: Enhanced Deep Super-Resolution Network. |
| - **Visual Comparison**: View the enhanced output for each model side-by-side. |
| - **Performance Metrics**: |
| - **Processing Time**: Time taken by each model to process the image. |
| - **Image Quality**: |
| - **PSNR** (Peak Signal-to-Noise Ratio): Measures the quality of the enhanced image compared to the original. |
| - **SSIM** (Structural Similarity Index): Assesses structural similarity between the images. |
| - **Download Enhanced Images**: Download the enhanced images generated by each model. |
|
|
| --- |
|
|
| ## Installation |
|
|
| ### Prerequisites |
|
|
| - Python 3.8 or above |
| - Pip package manager |
|
|
| ### Steps |
|
|
| 1. **Clone the Repository** |
| ```bash |
| git clone <repository-url> |
| cd <repository-folder> |
| ``` |
|
|
| 2. **Install Dependencies** |
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| 3. **Download Pre-trained Weights** |
| - Place the pre-trained model weights in the `checkpoints/` directory: |
| - `srcnn_best.pth` for SRCNN |
| - `vdsr_best.pth` for VDSR |
| - `edsr_best.pth` for EDSR |
|
|
| 4. **Run the Application** |
| ```bash |
| streamlit run app.py |
| ``` |
|
|
| --- |
|
|
| ## Usage |
|
|
| 1. Open the application in your browser (default URL: `http://localhost:8501`). |
| 2. Upload a low-resolution image using the file uploader. |
| 3. View the enhanced images generated by each model. |
| 4. Compare the performance and quality metrics for each model. |
| 5. Download the enhanced images for further use. |
|
|
| --- |
|
|
| ## File Structure |
|
|
| ``` |
| . |
| ├── app.py # Main Streamlit application script |
| ├── models/ # Directory for model definitions |
| │ ├── srcnn.py # SRCNN model |
| │ ├── vdsr.py # VDSR model |
| │ └── edsr.py # EDSR model |
| ├── checkpoints/ # Directory for storing pre-trained weights |
| │ ├── srcnn_best.pth |
| │ ├── vdsr_best.pth |
| │ └── edsr_best.pth |
| ├── requirements.txt # Python dependencies |
| └── README.md # Documentation |
| ``` |
|
|
| --- |
|
|
| ## Dependencies |
|
|
| Install the required Python libraries: |
| - `torch`: PyTorch for deep learning. |
| - `torchvision`: Image transformations and utilities. |
| - `streamlit`: Interactive web interface. |
| - `pillow`: Image processing library. |
| - `numpy`: Numerical operations. |
| - `scikit-image`: Image quality metrics (PSNR, SSIM). |
|
|
| --- |
|
|
| ## Models |
|
|
| ### 1. SRCNN |
| - A shallow neural network for super-resolution. |
| - Faster but may have lower performance on complex images. |
|
|
| ### 2. VDSR |
| - A deeper network providing improved results at the cost of longer processing time. |
|
|
| ### 3. EDSR |
| - Enhanced deep network specifically designed for high-quality super-resolution. |
|
|
| --- |
|
|
| ## Metrics |
|
|
| - **PSNR**: Measures the similarity of the enhanced image to the original in terms of pixel-level accuracy. Higher values indicate better quality. |
| - **SSIM**: Evaluates the structural similarity of the enhanced image to the original. Higher values indicate better preservation of structural features. |
|
|
| --- |
|
|
| ## Customization |
|
|
| ### Adding New Models |
| 1. Add the new model definition to the `models/` directory. |
| 2. Update the `load_model()` function in `app.py` to include the new model. |
|
|
| ### Updating Pre-trained Weights |
| Place updated weights in the `checkpoints/` directory with the naming convention `<model_name>_best.pth`. |
|
|
| --- |
|
|
| ## Future Enhancements |
|
|
| - Support for real-time video super-resolution. |
| - Integration with additional state-of-the-art super-resolution models. |
| - GPU acceleration for faster processing. |
|
|
| --- |
|
|
| ## License |
|
|
| This project is open-source and available under the MIT License. |
|
|
| --- |
|
|
| ## Contact |
|
|
| For queries or contributions, reach out via GitHub or email. |
|
|
| Happy enhancing! 🚀 |
|
|
