Benchmarking and Comparing Multi-exposure Image Fusion Algorithms

TL;DR

This paper introduces the first comprehensive benchmark for multi-exposure image fusion, providing a standardized test set, algorithms, metrics, and software to facilitate fair comparison and advance research in the field.

Contribution

It presents the first benchmark for MEF, including a test set, algorithm library, evaluation metrics, and software toolkit to standardize performance assessment.

Findings

Extensive evaluation of 16 algorithms using the benchmark.

Identification of the most effective algorithms.

Facilitation of fair comparison and future research in MEF.

Abstract

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.