SMFuse: Multi-Focus Image Fusion Via Self-Supervised Mask-Optimization

In this paper, a novel self-supervised mask-optimization model, termed as SMFuse, is proposed for multi-focus image fusion. In our model, given two source images, a fully end-to-end Mask-Generator is trained to directly generate the binary mask without requiring any patch operation or postprocessing through self-supervised learning. On the one hand, based on the principle of repeated blur, we design a Guided-Block with guided filter to obtain an initial binary mask from source images, narrowing the solution domain and speeding up the convergence of the binary mask generation, which is constrained by a map loss. On the other hand, as the focused regions in source images show richer texture details than the defocused ones, i.e., larger gradients, we also design a max-gradient loss between the fused image and source images as a follow-up optimization operation to ensure the fused image to be all-in-focus, forcing our model to learn a more accurate binary mask. Extensive experimental results conducted on two publicly available datasets substantiate the effectiveness and superiority of our SMFuse compared with the current state-of-the-art. Our code is publicly available online. ^{1 1} [Online]. Available: https://github.com/jiayi-ma/SMFuse.