ABSTRACT

Image fusion is an important technique for combining two or more images to produce a single, high-quality image. Principal component analysis (PCA) is a commonly used method for image fusion. However, existing PCA-based image fusion algorithms have some limitations, such as sensitivity to noise and poor fusion quality. In this paper, we propose a modified PCA algorithm for image fusion that uses an adaptive weighting scheme to improve the fusion quality. The proposed algorithm optimizes the fusion process by selecting the principal components that contain the most useful information and weighing them appropriately. Experimental results show that the proposed algorithm outperforms existing PCA-based image fusion algorithms in terms of fusion quality, sharpness, and contrast.

Keywords: - Image fusion, principle components analysis, adaptive weighting scheme, optimization, fusion quality, sharpness,contrast.