论文标题
多聚焦图像融合：基准测试
Multi-focus Image Fusion: A Benchmark
论文作者
论文摘要
多聚焦图像融合（MFIF）由于其众多应用而引起了相当大的兴趣。尽管近年来一直在开发各种MFIF算法方面取得了很多进展，但某些问题极大地阻碍了MFIF方法的公平和全面的性能比较，例如缺乏大规模测试集以及文献中客观评估指标的随机选择。为了解决这些问题，本文提出了一个多聚焦图像融合基准（MFIFB），该基准包括105个图像对的测试集，30 MFIF算法的代码库和20个评估指标。 MFIFB是MFIF领域的第一个基准，它为社区提供了一个平台，可以公平，全面地比较MFIF算法。已经使用拟议的MFIFB进行了广泛的实验，以了解这些算法的性能。通过分析实验结果，可以确定有效的MFIF算法。更重要的是，给出了一些关于MFIF字段状态的观察，这可以更好地理解该领域。
Multi-focus image fusion (MFIF) has attracted considerable interests due to its numerous applications. While much progress has been made in recent years with efforts on developing various MFIF algorithms, some issues significantly hinder the fair and comprehensive performance comparison of MFIF methods, such as the lack of large-scale test set and the random choices of objective evaluation metrics in the literature. To solve these issues, this paper presents a multi-focus image fusion benchmark (MFIFB) which consists a test set of 105 image pairs, a code library of 30 MFIF algorithms, and 20 evaluation metrics. MFIFB is the first benchmark in the field of MFIF and provides the community a platform to compare MFIF algorithms fairly and comprehensively. Extensive experiments have been conducted using the proposed MFIFB to understand the performance of these algorithms. By analyzing the experimental results, effective MFIF algorithms are identified. More importantly, some observations on the status of the MFIF field are given, which can help to understand this field better.
