Contourlet-Based Image Fusion

In this document, we introduce the contourlet-based image fusion method and emphasize its outstanding fusion performance. The approach typically involves implementing contourlet transform decomposition to capture directional edges and textures at multiple scales, followed by appropriate fusion rules to combine coefficients from source images. Key implementation steps include: - Applying contourlet transform to decompose input images into directional subbands - Designing fusion rules (such as maximum selection or weighted averaging) for coefficient combination - Reconstructing the fused image using inverse contourlet transform Furthermore, we can explore the underlying principles and application domains of this method. Notably, contourlet-based image fusion not only enhances image quality but also plays a significant role in various image processing applications such as medical imaging, remote sensing, and computer vision. Therefore, for individuals interested in image processing, understanding and mastering this technique proves highly beneficial, particularly for applications requiring precise texture preservation and edge information integration across multiple source images.