Dual-channel fusion and dual-discriminator GAN for infrared and visible image fusion

The fusion of infrared (IR) and visible (VIS) images aims to synthesize fused images with salient targets and enriched details. However, existing fusion methods face challenges in integrating modality-specific features. Accordingly, we proposed an image fusion method based on a dual-channel fusion strategy, termed DCGAN-Fuse. First, we created a dual-channel fusion strategy and constructed a dual-channel fusion module (DCFM) to integrate shared and complementary information across both modalities. Second, during the feature enhancement phase, we designed an attention-enhanced gradient retention module (AEGRM) to enhance edge feature extraction and enforce spatial consistency. Moreover, we used the multi-scale module (MSM) to capture fused features and avoid information loss from the two source images. We have improved the loss function by introducing the maximum intensity loss function for our proposed method. Experiments on public datasets demonstrated that our method generates fused images with highlighted infrared targets and enriched textures. Both subjective and objective assessments indicated that our DCGAN-Fuse is better than the other thirteen advanced algorithms.