Image fusion is a technique that combines multiple images with different focus areas into a single, high-quality image. This improves clarity, enhances details, and ensures that important features from all input images are retained. By leveraging Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), our approach minimizes distortions and preserves critical image information.
This method is widely used in medical imaging, remote sensing, surveillance, and security applications, where clear and detailed images are essential.
- ✅ Combines DWT and SWT for high-quality image fusion
- ✅ Retains fine details and sharpness in fused images
- ✅ Minimizes distortions and enhances clarity
- ✅ Preserves edges and spatial details for better feature extraction
- ✅ Ideal for multi-focus image fusion used in medical imaging, security, and surveillance
- ✅ Outperforms traditional fusion techniques with better accuracy in metrics like PSNR and SSIM
- Load Input Images – Provide multiple images with different focus levels.
- Apply Discrete Wavelet Transform (DWT) – Decomposes images into frequency bands.
- Apply Stationary Wavelet Transform (SWT) – Preserves image details without downsampling.
- Fusion Process – Combines DWT and SWT results to generate a high-quality image.
- Output the Final Fused Image – The resulting image retains critical features and improved clarity.
Ensure you have Python installed on your system. You also need the following libraries:
pip install numpy opencv-python pywt matplotlibgit clone https://github.com/yourusername/ImageFusion-Wavelet
cd ImageFusion-Waveletpython image_fusion.pyThis hybrid DWT + SWT approach significantly improves multi-focus image fusion by preserving essential details and reducing distortions. It is a powerful technique for various real-world applications requiring clear and accurate images.