This project acts as a central hub for researchers and enthusiasts interested in guided image restoration. You can use these tools to fix low-quality photos through smart algorithms. The software uses multi-modal data to help computers understand how to improve clarity. You will find resources for super-resolution, noise removal, and blur reduction in this collection.
Before you start, ensure your computer meets these basic needs:
- Operating System: Windows 10 or 11.
- Memory: 8 GB of RAM or more.
- Storage: 500 MB of free disk space.
- Graphics: A dedicated graphics card helps with processing speed but is not required.
You must visit the main project page to access the latest files. Use the link below to reach the official download source.
Click here to reach the project download page
Once you reach that page, look for the green button labeled "Code" and select "Download ZIP". This saves the collection of tools to your local drive.
Follow these steps to make the software ready for use on your Windows machine:
- Extract the Files: Open your Downloads folder. Right-click the file you just saved. Select "Extract All" from the menu. Choose a folder where you want to keep the toolset.
- Install Python: This project requires Python to run the restoration logic. Visit python.org and download the latest installer for Windows. Run the installer and check the box that says "Add Python to PATH" before you click install.
- Prepare the Environment: Open the folder where you extracted the files. Click inside the address bar at the top of the file window. Type "cmd" and press Enter. A black window appears.
- Install Dependencies: In that black window, type
pip install -r requirements.txtand press Enter. Wait for the computer to finish installing the necessary helper files.
After the setup finishes, you can start restoring your images.
- Open the Command Window: Go back to the main folder and open the black command window again by typing "cmd" in the address bar.
- Execute the Script: Each tool has a specific name. Most restoration scripts follow the pattern
python restore.py --input your_image.jpg. - Verify Output: The program creates a new file in your folder with the improved image. Open this file to preview the changes.
This repository organizes restoration tasks into clear categories:
- Super-Resolution: Use these models to increase the size and detail of small images.
- Denoising: Use these tools to remove grain or digital noise from low-light photos.
- Deblurring: These scripts sharpen images that suffer from camera shake or focus errors.
- Guided Enhancement: This advanced feature uses a secondary image as a reference to guide the restoration process.
- Choose High-Quality Inputs: The output is only as good as the input file. Start with the best version of your photo.
- Use GPU Support: If your computer has an NVIDIA graphics card, run your restoration tasks using the GPU flag to see faster performance.
- Check File Paths: When you type the location of your image, make sure you use the full name, including the file extension like .jpg or .png.
- Manage Large Files: Some models require a lot of memory. If the program stops, close other open heavy applications.
If you run into issues, check these common items:
- Command not found: This means Python is not in your computer path. Reinstall Python and ensure you check the "Add to PATH" box.
- Missing Libraries: If the program reports an error about a missing file, run the
pip install -r requirements.txtcommand one more time. - Slow Processing: Image restoration requires heavy math. On older computers, some images may take several minutes to process. Please wait for the command window to show a completion message.
Keywords: image restoration, super-resolution, enhancement, denoising, deblurring, guided restoration, computer vision