Image fusion algorithm based on unsupervised deep learning-optimized sparse representation

The image fusion method based on deep learning has problems such as the supervised learning of the model, the edge and noise of the fused image, and the setting of the image fusion weight map. To solve these problems, this paper proposes an end-to-end unsupervised deep learning model that performs one-to-many focus image fusion. It solves the training problem encountered by supervised deep learning models while avoiding the unreasonable image fusion weight maps. In addition, this paper proposes an optimized sparse representation method that divides an image into a target area and a background area. Then, it uses super complete dictionary learning to obtain a sparse representation of the image background area. This approach makes the proposed unsupervised deep learning image fusion method robust to noise. Finally, using this method to carry out image fusion experiments, the results show that the quality evaluation indicators of the fused image obtained by this method substantially outperform those of both mainstream machine learning and deep learning image fusion methods.

Automated summary

This paper proposed an end-to-end unsupervised deep learning model for image fusion, which addresses the issues of supervised learning, image fusion weight map setting and noise. An optimized sparse representation method was also introduced to further improve the quality of the fusion results. The experiments demonstrated that the proposed method outperformed mainstream machine learning and deep learning image fusion methods in terms of quality evaluation.