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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15050

Title: Optimal Block-based Feature-level Multi-focus Image Fusion
Authors: Abdul Basit Siddiqui
Arfan Jaffar
Ayyaz Hussain
Anwar M. Mirza
Keywords: Fusion, Optimal Block, NN
Issue Date: 2010
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
URI: http://hdl.handle.net/123456789/15050
Appears in Collections:College of Computer and Information Sciences

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