A novel ensemble scheme for the multi-focus image fusion using support vector machine.

In the multi-focus image fusion problem, the source images are obtained from the same scene. They are fused to get an image that contains all well-focussed objects. Previously, individual machine-learning models are proposed for image fusion. The performance of individual models is limited to fuse the useful information extracted from the blurred images. To address this problem, we developed a novel ensemble scheme for multi-focus image fusion using support vector machines (SVMs). In the proposed scheme, first, SVM models are constructed using different kernel functions of linear, polynomial, radial basis, and sigmoid. The predictions of individual SVM models are then combined using majority voting. In this way, the combined decision space becomes more informative and discriminant. A comparative analysis of the proposed scheme is carried out with previous techniques. It is found that our scheme is more accurate for synthesized-blurred and real defocussed images. [ABSTRACT FROM AUTHOR]