A Deep Fusion Rule for Infrared and Visible Image Fusion: Feature Communication for Importance Assessment

The purpose of infrared and visible image fusion is to extract and combine information from source images to produce results that contain vital and complementary information. Existing fusion rules may not extract the most useful information and cannot effectively retain important information. To solve this problem, we propose a novel deep learning-based fusion rule. We perform deep feature communication and quantify the effect of feature substitution on image characteristics, including gradients and contrast. Based on the influence, the importance of feature maps can be objectively assessed. This designed fusion rule can preserve the thermal information of infrared images and the texture details of visible light images in a targeted manner, so as to obtain better fusion results. Qualitative and quantitative experiments have shown that our method can perform better than other advanced methods.