论文标题
基准测试和比较多曝光图像融合算法
Benchmarking and Comparing Multi-exposure Image Fusion Algorithms
论文作者
论文摘要
多曝光图像融合（MEF）是计算机视觉中的重要领域，近年来吸引了越来越多的兴趣。除了常规的算法外，还将深度学习技术应用于多曝光图像融合。但是，尽管为开发MEF算法做出了许多努力，但缺乏基准使MEF算法之间的公平和全面的性能比较变得困难，因此极大地阻碍了该领域的发展。在本文中，我们通过提出一个用于多曝光图像融合（MEFB）的基准来填补这一空白，该基准由100个图像对的测试集，16个算法的代码库，20个评估指标，1600个融合图像和软件工具包组成。据我们所知，这是多曝光图像融合领域的第一个基准。已经使用MEFB进行了广泛的实验，以进行全面的性能评估和识别有效算法。我们预计MEFB将成为研究人员比较性能和研究MEF算法的有效平台。
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 multi-exposure image fusion. However, although much efforts have been made on developing MEF algorithms, the lack of benchmark makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus significantly hindering the development of this field. In this paper, we fill this gap by proposing a benchmark for multi-exposure image fusion (MEFB) which consists of a test set of 100 image pairs, a code library of 16 algorithms, 20 evaluation metrics, 1600 fused images and a software toolkit. To the best of our knowledge, this is the first benchmark in the field of multi-exposure image fusion. 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 performances and investigate MEF algorithms.
