In this paper, we propose TransMEF, a transformer-based multi-exposure image
fusion framework that uses self-supervised multi-task learning. The framework
is based on an encoder-decoder network, which can be trained on large natural
image datasets and does not require ground truth fusion images. We design three
self-supervised reconstruction tasks according to the characteristics of
multi-exposure images and conduct these tasks simultaneously using multi-task
learning; through this process, the network can learn the characteristics of
multi-exposure images and extract more generalized features. In addition, to
compensate for the defect in establishing long-range dependencies in CNN-based
architectures, we design an encoder that combines a CNN module with a
transformer module. This combination enables the network to focus on both local
and global information. We evaluated our method and compared it to 11
competitive traditional and deep learning-based methods on the latest released
multi-exposure image fusion benchmark dataset, and our method achieved the best
performance in both subjective and objective evaluations.

本文提出了一种基于 Transformer 的多曝光图像融合框架 TransMEF，使用自监督多任务学习，通过设计重构任务并使用多任务学习进行同时训练，使网络可以学习到多曝光图像的特征和提取更广义的特征，同时将 CNN 模块与 Transformer 模块结合来解决 CNN-based 构架中建立长距离依赖的缺陷。在对比 11 种传统和深度学习方法后，本文提出的方法在主观和客观评估中都取得了最佳性能。