Infrared and visible image fusion in a rolling guided filtering framework based on deep feature extraction

To preserve rich detail information and high contrast, a novel image fusion algorithm is proposed based on rolling-guided filtering combined with deep feature extraction. Firstly, input images are filtered to acquire various scales decomposed images using rolling guided filtering. Subsequently, PCANet is introduced to extract weight maps to guide base layer fusion. For the others layer, saliency maps of input images are extracted by a saliency measure. Then, the saliency maps are optimized by guided filtering to guide the detail layer fusion. Finally, the final fusion result are reconstructed by all fusion layers. The experimental fusion results demonstrate that fusion algorithm in this study obtains following advantages of rich detail information, high contrast, and complete edge information preservation in the subjective evaluation and better results in the objective evaluation index. In particular, the proposed method is 16.9% ahead of the best comparison result in the SD objective evaluation index.