Benchmarking and Comparing Multi-exposure Image Fusion Algorithms
This provides a standardized platform for researchers in computer vision to compare MEF algorithms, addressing a critical gap that hindered development in the field.
The authors tackled the lack of a benchmark for multi-exposure image fusion (MEF) by proposing MEFB, which includes 100 image pairs, 16 algorithms, 20 metrics, 1600 fused images, and a software toolkit, enabling comprehensive performance evaluation and identification of effective algorithms.
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.