Infrared and visible image fusion with ResNet and zero-phase component analysis

In image fusion approaches, feature extraction and processing are key tasks, and the fusion performance is directly affected by the different features and processing methods undertaken. However, most of deep learning-based methods use deep features directly without them. This leads to the fusion performance degradation in some cases. To solve these drawbacks, in our paper, a deep features and zero-phase component analysis (ZCA) based novel fusion framework is proposed. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA and l(1)-norm are utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed framework achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at https://github.com/hli1221/imagefusion_resnet50.