This paper proposes a new generative adversarial network for infrared and visible image fusion based on semantic segmentation (SSGAN), which can consider not only the low-level features of infrared and visible images, but also the high-level semantic information. Source images can be divided into foregrounds and backgrounds by semantic masks. The generator with a dual-encoder-single-decoder framework is used to extract the feature of foregrounds and backgrounds by different encoder paths. Moreover, the discriminator’s input image is designed based on semantic segmentation, which is obtained by combining the foregrounds of the infrared images with the backgrounds of the visible images. Consequently, the prominence of thermal targets in the infrared images and texture details in the visible images can be preserved in the fused images simultaneously. Qualitative and quantitative experiments on publicly available datasets demonstrate that the proposed approach can significantly outperform the state-of-the-art methods.

Infrared and visible image fusion needs to preserve both the salient target of the infrared image and the texture details of the visible image. Therefore, an infrared and visible image fusion method based on saliency detection is proposed. Firstly, the saliency map of the infrared image is obtained by saliency detection. Then, the specific loss function and network architecture are designed based on the saliency map to improve the performance of the fusion algorithm. Specifically, the saliency map is normalized to [0, 1], used as a weight map to constrain the loss function. At the same time, the saliency map is binarized to extract salient regions and nonsalient regions. And, a generative adversarial network with dual discriminators is obtained. The two discriminators are used to distinguish the salient regions and the nonsalient regions, respectively, to promote the generator to generate better fusion results. The experimental results show that the fusion results of our method are better than those of the existing methods in both subjective and objective aspects.