Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to MEF. However, although many efforts have been made on developing MEF algorithms, the lack of benchmarking studies makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus hindering the development of this field significantly. In this paper, we fill this gap by proposing a benchmark of multi-exposure image fusion (MEFB), which consists of a test set of 100 image pairs, a code library of 21 algorithms, 20 evaluation metrics, 2100 fused images, and a software toolkit. To the best of our knowledge, this is the first benchmarking study in the field of MEF. This paper also gives a literature review on MEF methods with a focus on deep learning-based algorithms. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare the performance of MEF algorithms.

多重曝光图像融合（MEF）是计算机视觉中的重要领域，并且近年来引起了越来越多的兴趣。除传统算法外，深度学习技术也已应用于MEF。然而，尽管在开发MEF算法方面已经付出了很多努力，但是由于缺乏基准测试的研究，使得在MEF算法之间进行公平，全面的性能比较变得困难，从而严重阻碍了该领域的发展。在本文中，我们通过提出一个多重曝光图像融合（MEFB）基准来填补这一空白，该基准包括100个图像对的测试集，21个算法的代码库，20个评估指标，2100个融合图像以及一个软件工具包。据我们所知，这是MEF领域中的第一项基准研究。本文还提供了有关MEF方法的文献综述，重点是基于深度学习的算法。使用MEFB进行了广泛的实验，以进行全面的性能评估并确定有效的算法。我们希望MEFB将成为研究人员比较MEF算法性能的有效平台。