Edafuse : A Encoder-Decoder with Atrous Spatial Pyramid Network for Infrared and Visible Image Fusion

Infrared and visible images come from different sensors, and they have their advantages and disadvantages. In order to make the fused images contain as much salience information as possible, a practical fusion method, termed as EDAfuse, is proposed in this paper. In EDAfuse, we introduce an encoder-decoder with the atrous spatial pyramid network for infrared and visible image fusion. We use the encoding network which includes three CNN layers to extract deep features from input images. Then the proposed atrous spatial pyramid model is utilized to get five different scale features. The same scale features from the two original images are fused by our fusion strategy with the attention model and information quantity model. Finally, the decoding network is utilized to reconstruct the fused image. In the training process, we introduce a loss function with saliency loss to improve the ability of the model for extracting salient features from original images. Experiment results on publicly available datasets demonstrate that the proposed method outperforms the state-of-the-art fusion methods in subjective and objective assessment