BLOCK-BASED PIXEL LEVEL MULTI-FOCUS IMAGE FUSION USING PARTICLE SWARM OPTIMIZATION

摘要

For accurate image segmentation, edge detection and stereo matching, it is significant that all the objects in the image under processing must be in focus. However, due to limited depth of field of optical lenses particularly which have greater focal length, it is not always possible. In such cases, image fusion is performed to obtain an everywhere-in focus image. In this paper, we have proposed a highly precise method for multi-focus image fusion. We have proposed a method based on Particle Swarm Optimization (PSO) to find out the optimal size of blocks to be fused. Detailed experimentation is performed using different quantitative measures for different set of multi-focus images. We have compared the results of proposed technique with different existing image fusion techniques such as DWT, aDWT, PC A and Laplacian Pyramid based image fusion. Experimental results show that the proposed method outperforms the traditional approach both visually and quantitatively