High-Resolution and Low-Resolution Image Fusion Using RGB-IHS Transformation with Algorithm Implementation

The RGB-IHS transformation method enables effective fusion of high-resolution and low-resolution images to produce clearer and more detailed image results. This technique utilizes color space conversion algorithms where RGB (Red-Green-Blue) color space is transformed into IHS (Intensity-Hue-Saturation) components. The core implementation involves separating spatial information (intensity) from spectral information (hue and saturation), allowing the integration of high-resolution spatial details with low-resolution spectral characteristics. Key algorithmic steps include: 1. Converting input RGB images to IHS color space using transformation matrices 2. Replacing the intensity component of the low-resolution image with the high-resolution image's spatial details 3. Performing inverse IHS-to-RGB transformation to generate the fused output 4. Applying histogram matching or adaptive weighting techniques for optimal spectral preservation This technology finds applications across multiple domains including medical imaging, remote sensing, and security surveillance systems. By combining the detailed spatial information from high-resolution images with the global spectral information from low-resolution images, we significantly enhance image quality and analytical accuracy. The fusion process improves both visual interpretation and machine analysis capabilities. The RGB-IHS transformation approach for image fusion provides enhanced informational content and finer details, substantially improving visual quality and recognition performance. This method represents a robust and efficient solution for generating enriched and accurate image information, with implementation typically involving matrix operations and color space conversion functions in image processing libraries.