  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.
