MATLAB Code Implementation for Image Fusion

Image fusion technology integrates information from multiple images into a single composite image, enhancing both image quality and practical application value. In MATLAB, various methods can be implemented for image fusion, including high-pass filtering, IHS transformation, Principal Component Analysis (PCA), wavelet transform, and hybrid wavelet-IHS approaches. High-Pass Filtering Method High-pass filtering extracts high-frequency detail information from images, typically combined with low-pass filtering for fusion. This method is suitable for enhancing edges and textures but may be sensitive to noise. In MATLAB implementation, functions like fspecial('laplacian') or imfilter() can be used to create and apply high-pass filters, with the fusion process often involving weighted combinations of filtered components. IHS Transformation Method IHS (Intensity-Hue-Saturation) transformation is a color space conversion approach frequently used in remote sensing image fusion. It converts RGB images to Intensity (I), Hue (H), and Saturation (S) components, allowing replacement of intensity information before converting back to RGB space to improve fusion results. MATLAB implementation typically uses rgb2ihs() or similar color space conversion functions, followed by component substitution and inverse transformation. Principal Component Analysis (PCA) Method PCA extracts key features for fusion by computing principal components of images. This method effectively reduces data redundancy but may sacrifice some spectral information. MATLAB's pca() function or princomp() can be employed to compute eigenvectors and eigenvalues, with fusion achieved by replacing principal components and reconstructing the image. Wavelet Transform Method Wavelet transform analyzes images in time-frequency domain, making it suitable for multi-resolution fusion. By decomposing and reconstructing coefficients from different frequency bands, it preserves more detailed information with superior fusion quality. MATLAB's wavelet toolbox provides functions like wavedec2() for decomposition and waverec2() for reconstruction, enabling flexible coefficient blending strategies. Wavelet-IHS Hybrid Method Combining wavelet transform's multi-resolution analysis with IHS's color preservation advantages further enhances fusion quality. This approach improves spatial details while maintaining good spectral characteristics, making it ideal for high-precision fusion requirements. Implementation in MATLAB typically involves performing IHS transformation first, then applying wavelet decomposition to the intensity component, followed by selective coefficient fusion and reconstruction. When implementing these methods in MATLAB, developers can utilize built-in Image Processing Toolbox functions or create custom algorithms. Parameter optimization through iterative testing helps achieve optimal fusion results, with applications spanning remote sensing, medical imaging, and various other fields requiring multi-source image integration.