  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.
