Multi-focus image fusion using boosted random walks-based algorithm with two-scale focus maps

In conventional fusion methods for multi-focus images, the focus map generated by a focus measure would usually be sensitive to mis-registration and noise, or produce badly-aligned boundaries. While many state-of-the-art algorithms use more complex strategies or procedures to address this problem, in this paper we propose to estimate a focus map directly from the two-scale imperfect observations (focus maps) obtained using a small and large-scale focus measures. This would contribute to a more robust fusion by taking advantage of the complementary properties of the two-scale observed focus maps, i.e., robustness to mis-registration (and noise) and the better aligned boundaries. The estimation is firstly modeled in a probabilistic perspective using random walks-based algorithm, in which we try to solve the probabilities that each pixel of the focus map is associated with the observed ones. Then we found that this method is equivalent to solving an alternate objective function, enabling a great boost both in computational efficiency and estimation result. Experimental results demonstrate that the proposed method is robust yet efficient compared with state-of-the-art fusion methods.