论文标题
通过基于优化的策略来减少多聚焦图像融合的严重散焦传播效应
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
论文作者
论文摘要
多聚焦图像融合（MFF）是一种流行的技术，可以生成全焦点图像，场景中的所有对象都很清晰。但是，现有方法几乎不关注现实世界多聚焦图像的散焦差异效应。因此，大多数方法在焦点地图边界附近的区域中表现不佳。根据融合图像中的每个局部区域应类似于源图像中最锐利的局部区域的想法，本文提出了一种基于优化的方法来减少散焦扩展效应。首先，通过结合结构相似性的原理和检测到的焦点图来提出新的MFF评估表。然后，MFF问题将最大化该指标。通过梯度上升来解决优化。在现实世界数据集上进行的实验验证了所提出的模型的优势。这些代码可在https://github.com/xsxjtu/mff-ssim上找到。
Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image, where all objects in the scene are sharp. However, existing methods pay little attention to defocus spread effects of the real-world multi-focus images. Consequently, most of the methods perform badly in the areas near focus map boundaries. According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects. Firstly, a new MFF assessmentmetric is presented by combining the principle of structure similarity and detected focus maps. Then, MFF problem is cast into maximizing this metric. The optimization is solved by gradient ascent. Experiments conducted on the real-world dataset verify superiority of the proposed model. The codes are available at https://github.com/xsxjtu/MFF-SSIM.
