  Feature extraction and processing tasks play a key role in Image Fusion, and
the fusion performance is directly affected by the different features and
processing methods undertaken. By contrast, most of deep learning-based methods
use deep features directly without feature extraction or processing. This leads
to the fusion performance degradation in some cases. To solve these drawbacks,
we propose a deep features and zero-phase component analysis (ZCA) based novel
fusion framework is this paper. Firstly, the residual network (ResNet) is used
to extract deep features from source images. Then ZCA is 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
