An Image Fusion Method Based on Quotient Singular Value Decomposition

This paper presents a new image fusion algorithm that combines quotient singular value decomposition (QSVD) with a simple averaging operation. Multi-focused images are first averaged into a new image. Then, error images are obtained with the averaged image and the multi-focused images. The most error-contributing component in each error image is replaced by the most contributing image component in the multi-focused image using QSVD in order to reduce errors. With each reduced error image, a new singular vector is calculated to get fused images. The final infused image is then decided by calculating the standard deviation of each fused image. Experiment results such as mutual information (MI), information entropy (IE), edge preservation information (Q(abf)), signal-to-noise-ratio (SNR) and root mean square error (RMSE) are used to evaluate the algorithm. The experimental results show that the developed algorithm is an efficient fusion algorithm.