  Image fusion is a technique to integrate information from multiple source
images with complementary information to improve the richness of a single
image. Due to insufficient task-specific training data and corresponding ground
truth, most existing end-to-end image fusion methods easily fall into
overfitting or tedious parameter optimization processes. Two-stage methods
avoid the need of large amount of task-specific training data by training
encoder-decoder network on large natural image datasets and utilizing the
extracted features for fusion, but the domain gap between natural images and
different fusion tasks results in limited performance. In this study, we design
a novel encoder-decoder based image fusion framework and propose a
destruction-reconstruction based self-supervised training scheme to encourage
the network to learn task-specific features. Specifically, we propose three
destruction-reconstruction self-supervised auxiliary tasks for multi-modal
image fusion, multi-exposure image fusion and multi-focus image fusion based on
pixel intensity non-linear transformation, brightness transformation and noise
transformation, respectively. In order to encourage different fusion tasks to
promote each other and increase the generalizability of the trained network, we
integrate the three self-supervised auxiliary tasks by randomly choosing one of
them to destroy a natural image in model training. In addition, we design a new
encoder that combines CNN and Transformer for feature extraction, so that the
trained model can exploit both local and global information. Extensive
experiments on multi-modal image fusion, multi-exposure image fusion and
multi-focus image fusion tasks demonstrate that our proposed method achieves
the state-of-the-art performance in both subjective and objective evaluations.
The code will be publicly available soon.
