Update README.md

README.md

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----
-license: mit
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+# 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! 🚀