Optimal Block-based Feature-level Multi-focus Image Fusion

Abstract

In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Image fusion deals with creating an image in which all the objects are in focus. Thus it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. In this paper, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images using classification. Ten pairs of multi-focus images are first divided into blocks. The optimal block size for every image is found adaptively. The block feature vectors are fed to feed forward neural network. The trained neural network is then used to fuse any pair of multi-focus images. We have also presented the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique