Multi-focus image fusion with PCA filters of PCANet

Published in: IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer Interaction

The paper links

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.

The framework of fusion method

citation

For paper:

Song X, Wu X J. Multi-focus Image Fusion with PCA Filters of PCANet[C]//IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer Interaction. Springer, Cham, 2018: 1-17.

For code:

@inproceedings{song2018multi,
  title={Multi-focus Image Fusion with PCA Filters of PCANet},
  author={Song, Xu and Wu, Xiao-Jun},
  booktitle={IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer Interaction},
  pages={1--17},
  year={2018},
  organization={Springer}
  howpublished = {\url{https://github.com/songxujay/Multi-focus-image-fusion-with-PCA-filters-of-PCANet}}
}

Acknowledgment

Many thanks to Professor Yi Ma in the homepage to provide PCANet source code

Many thanks to Professor Xiao-Jun Wu and Dr.Hui Li