EMEF: Ensemble Multi-Exposure Image Fusion
Renshuai Liu Chengyang Li Haitao Cao Yinglin Zheng Ming Zeng Xuan Cheng
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 https://github.com/medalwill/EMEF.