Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 209-215.DOI: 10.3778/j.issn.1002-8331.2007-0034

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Multi-focus Image Fusion Algorithm Based on Unsupervised Deep Learning Model

WANG Changcheng, ZHOU Dongming, LIU Yanyu, XIE Shidong   

  1. School of Information Science & Engineering, Yunnan University, Kunming 650504, China
  • Online:2021-11-01 Published:2021-11-04



  1. 云南大学 信息学院,昆明 650504


When deep learning technology is applied to the field of multi-focus image fusion, it mostly trains the network by supervised learning. However, due to the lack of labeled data sets of multi-focus image fusion for supervised training, and the cost of making a dedicated large-scale labeled training set is too high. The existing methods mostly simulate multi-focus image for supervised learning by adding Gaussian blur to the focused image, which makes network training difficult and difficult to achieve the ideal fusion effect. In society to solve the above problems, a multi-focus image fusion method with easy implementation and good fusion effect is proposed. Encoder-Decoder network model with attention mechanism is introduced by training in an unsupervised learning way on easily accessible unlabeled data set to obtain the rich features of the input source images. And then the initial decision map is generated by measuring the activity level of these features through multi-scale morphological focus detection. Eventually, the consistency verification method is used to adjust and optimize the initial decision map, and the final decision map is obtained. The quality of the fusion image is valued on both subjective vision and objective indicators. The experimental results demonstrate that the fusion image has high definition, rich details and low distortion.

Key words: deep learning, multi-focus image fusion, supervised learning, unsupervised learning, morphological focus detection



关键词: 深度学习, 多聚焦图像融合, 监督学习, 无监督学习, 形态聚焦检测