Multi-focus image fusion algorithm based on random features embedding and ensemble learning

Abstract

Multi-focus image fusion algorithm integrates complementary information from multiple source images to obtain an all-in-focus image. Most published methods will create incorrect points in their decision map which have to be refined and polished with post-processing procedure. Aim to address these problems, we present, for the first time, a novel algorithm based on random features embedding (RFE) and ensemble learning which reduced the calculation workload and improved the accuracy without post-processing. We utilize RFE to approximate a kernel function so that Support Vector Machine (SVM) can be applied to large scale data set. With ensemble learning scheme we then eliminate the abnormal points in the decision map. We reduce the risk of entrap into over-fitting predicament and boost the generalization ability by combining RFE and ensemble learning. The theoretical analysis is in consistence with the experimental results. With low computation cost, the proposed algorithm achieve high visual quality as the state-of-the-art(SOTA).