JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (3): 7-13.doi: 10.6040/j.issn.1672-3961.2.2015.106

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A new multi-focus image fusion method based on deep neural network model

LIU Fan, CHEN Zehua, CHAI Jing   

  1. College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • Received:2015-06-23 Online:2016-06-30 Published:2015-06-23

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Abstract: There existed low-frequency information distortion phenomenon in fusing multi-focus images. Aimed to solve the problem, a new fusion strategy based on deep neural network model was proposed for fusing low-frequency subbands. Combined with Wavelet Kernel Filter and traditional fusion strategy for high-frequency subbands, a new fusion method for fusing multi-focus images was given. The method extracted efficient features by using AutoEncoder model. The experimental results showed that proposed method could obtain better images. The edge fusion qualify value of the proposed fusion result was 0.802 7, compared with traditional fusion strategy, contourlet-based multi-focus method and non-sampled contourlet-based multi-focus method, 0.761 4, 0.722 7, and 0.716 4, which could provide an effective method for fusing multi-focus images.

Key words: autoencoder, deep neural network, image fusion, multi-focus image, wavelet kernel filter

CLC Number: 

  • TP183
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