Although remarkable progress has been made in recent years, current
multi-exposure image fusion (MEF) research is still bounded by the lack of real
ground truth, objective evaluation function, and robust fusion strategy. In
this paper, we study the MEF problem from a new perspective. We don't utilize
any synthesized ground truth, design any loss function, or develop any fusion
strategy. Our proposed method EMEF takes advantage of the wisdom of multiple
imperfect MEF contributors including both conventional and deep learning-based
methods. Specifically, EMEF consists of two main stages: pre-train an imitator
network and tune the imitator in the runtime. In the first stage, we make a
unified network imitate different MEF targets in a style modulation way. In the
second stage, we tune the imitator network by optimizing the style code, in
order to find an optimal fusion result for each input pair. In the experiment,
we construct EMEF from four state-of-the-art MEF methods and then make
comparisons with the individuals and several other competitive methods on the
latest released MEF benchmark dataset. The promising experimental results
demonstrate that our ensemble framework can "get the best of all worlds". The
code is available at this https URL

本文提出了一种基于多个不完美的多曝光图像融合贡献者的集合框架（EMEF）以解决当前的 MEF 问题，通过在预训练阶段统一网络模仿不同的 MEF 目标，并在运行时通过优化风格代码来调整模仿者网络，进而在最新的 MEF 基准测试数据集上取得了最好的实验结果。