| # Neural Style Transfer (NST) for Image Enhancement | |
| Enhance your images using Neural Style Transfer by combining the content of an input image with the style of a reference image. | |
| ## Description | |
| This project uses TensorFlow to perform Neural Style Transfer (NST) on an input image using a style reference image. NST is a technique for enhancing an image by transferring the artistic style of one image (the reference style image) to the content of another image (the input image). The result is a new image that combines the content of the input image with the artistic style of the reference image. | |
| ## Prerequisites | |
| - Python 3.x | |
| - TensorFlow 2.x | |
| - NumPy | |
| - Matplotlib | |
| - Pillow | |
| You can install the required Python packages by running: | |
| ## Usage | |
| 1. Prepare your input image and style reference image and save them in the project directory. | |
| 2. Update the paths to your input and style reference images in the script (`input_image_path` and `style_image_path` variables). | |
| 3. Run the script: | |
| 4. The script will optimize the generated image to combine the content of the input image with the style of the reference image. | |
| 5. The final enhanced image will be saved as `enhanced_image.jpg` in the project directory. | |
| ## Examples | |
| Here are some example results of using NST to enhance images: | |
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| ## License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |
| ## Acknowledgments | |
| - This project is based on the Neural Style Transfer technique developed by Gatys et al. | |
| - Pre-trained VGG models provided by the Keras team. | |
| Feel free to modify this README file to include more details, usage instructions, or additional sections relevant to your project. | |