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.

中文翻译：

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SMFuse：通过自我监督的蒙版优化实现多焦点图像融合

本文提出了一种新的自监督模板优化模型，称为SMFuse，用于多焦点图像融合。在我们的模型中，给定两个源图像，训练了一个完整的端到端Mask-Generator，可以直接生成二进制掩码，而无需任何补丁操作或通过自我监督学习进行后处理。一方面，基于重复模糊的原理，我们设计了带有导引滤波器的导引块，以从源图像获得初始二进制掩码，从而缩小了求解域，并加快了二进制掩码生成的收敛速度，这受到了限制。由于地图丢失。另一方面，由于源图像中的聚焦区域显示的图像细节比散焦图像的丰富，IE 较大的梯度，我们还设计了融合图像和源图像之间的最大梯度损失，作为后续的优化操作，以确保融合图像是全焦点的，从而迫使我们的模型学习更准确的二值蒙版。与当前的最新技术相比，在两个可公开获得的数据集上进行的大量实验结果证实了我们SMFuse的有效性和优越性。我们的代码可在线公开获得。 ^{1个 1个}

[在线的]。可用的：https://github.com/jiayi-ma/SMFuse。