Abstract:
Aiming at the defect of making use of hand-crafted features and fusion criterions to fulfill the fusion task by traditional methods, which does not consider efficiently other potentially useful information in source images, we propose a deep learning method based on the spatial pyramid pooling (SPP). First, we design a Siamese network and replace the average pooling with SPP to learn the features of multi-focus images. Then, to train the network effectively, we synthesize a large-scale multi-focus image dataset with ground truth through a Gaussian filter. Given a pair of multi-focus image as input, the trained model can generate a score map indicating the focus property of source images. Moreover, to further enhance the fusion effects, we segment the score map into a binary mask image, which is refined using morphological technique. Finally, the fused image is gained by employing dot multiplication operation between source images and the refined binary mask image. Experimental results reveal that the average quantitative score on test images achieved by the proposed method is increased by 0.78%.