DCFusion: A Dual-Frequency Cross-Enhanced Fusion Network for Infrared and Visible Image Fusion

The visible image contains many high-frequency components that provide texture details with high spatial resolution and definition consistent with human visual perception, but it is easily affected by external factors such as light, weather, and obstructions. On the other hand, the infrared image is a radiation image whose contrast is determined by the temperature difference between the target and the background, and is not easily affected by external conditions. Integrating complementary information from both image types into one image is therefore very useful. In our paper, we propose a dual-frequency cross-enhanced fusion network called DCFusion for infrared and visible image fusion. We design a frequency decomposition module and a frequency enhancement module based on Laplacian of Gaussian for feature decomposition and enhancement, respectively. We then build a dual-frequency cross-enhanced fusion generator network based on these two modules to achieve enhanced fusion. We also use the sum of visible and infrared discriminator and the visible discriminator to balance our fusion results, replacing the traditional single visible discriminator. Our method is an end-to-end model, avoiding the manual design of complex fusion rules like traditional methods. Compared with existing advanced fusion algorithms, our method outperforms most of them in qualitative comparison, quantitative comparison, and target detection accuracy. Finally, the experiment proves that our method can effectively enhance the fusion of the target scene even in harsh environments such as complex lighting, low illumination, and smoke scenes.