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Multi-focus Image Fusion with PCA Filters of PCANet

  • Xu Song
  • Xiao-Jun Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11377)

Abstract

As is well known to all, the training of deep learning model is time consuming and complex. Therefore, in this paper, a very simple deep learning model called PCANet is used to extract image features from multi-focus images. First, we train the two-stage PCANet using ImageNet to get PCA filters which will be used to extract image features. Using the feature maps of the first stage of PCANet, we generate activity level maps of source images by using nuclear norm. Then, the decision map is obtained through a series of post-processing operations on the activity level maps. Finally, the fused image is achieved by utilizing a weighted fusion rule. The experimental results demonstrate that the proposed method can achieve state-of-the-art fusion performance in terms of both objective assessment and visual quality.

Keywords

Multi-focus image fusion PCA filters Nuclear norm 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xu Song
    • 1
  • Xiao-Jun Wu
    • 1
  1. 1.Jiangsu Provincial Engineering Laborator of Pattern Recognition and Computational IntelligenceJiangnan UniversityWuxiChina

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