Multi-focus Image Fusion Method Based on NSST and IICM

Abstract

Multi-focus image fusion is a classic issue in the field of image processing. How to extract and fuse the in-focus information from the source images into the single one is the key to resolving the above problem. As a novel multi-resolution analysis tool, non-subsampled shearlet transform (NSST) not only has better information capturing ability, but also owns a comparatively lower computational complexity compared with non-subsampled contourlet transform (NSCT). Intersecting cortical model (ICM) is the third generation of artificial neural network, and it can be viewed as the improved version of pulse-coupled neural network. The superiority of ICM lies in that it has much fewer parameters and better function mechanism. In this paper, a novel method for multi-focus image fusion based on NSST and improved ICM is presented. On the one hand, NSST is responsible for decomposing source images and reconstructing sub-images. On the other hand, ICM is used to complete the coefficients selecting of sub-images. Experimental results demonstrate that the proposed method has better performance compared with the current typical ones.