CCFusion: an infrared and visible image fusion method based on cycle consistency and covariance compression disentanglement module

Thanks to the feature disentanglement capability of disentangled representation methods, deep learning-based de-entanglement methods have achieved strong performance in image fusion tasks. However, it should be noted that training a powerful performance of a disentangled representation method is a challenging problem due to the domain differences between image fusion tasks and mainstream disentanglement tasks. To overcome this problem, an infrared and visible image fusion method based on cycle consistency and the covariance compression disentanglement module proposed in this manuscript. Firstly, the proposed covariance compression disentanglement module statistically counts the global information by global covariance pool and adaptively enhances the desired features and suppresses others by the covariance compression module. Secondly, it makes the disentanglement module cycle-consistent by disentanglement the fusion image, and strengthens the disentanglement by the proposed consistency loss function and feature similarity loss function constraints. Specifically, in this paper, multi-source images are input into the disentanglement module, modal features and common features are disentangled, and fusion results are generated by fusing the modal features and common features of multi-source images. After generating the fusion result, the fusion result is input into the disentanglement module again, and the re-decomposed features are used to calculate the consistency loss with the multi-source image features. On the publicly available TNO dataset, subjective and objective evaluations show that the method proposed in this manuscript has better fusion performance than other state-of-the-art methods.