SESF-Fuse: an unsupervised deep model for multi-focus image fusion

Muti-focus image fusion is the extraction of focused regions from different images to create one all-in-focus fused image. The key point is that only objects within the depth-of-field have a sharp appearance in the photograph, while other objects are likely to be blurred. We propose an unsupervised deep learning model for multi-focus image fusion. We train an encoder-decoder network in an unsupervised manner to acquire deep features of input images. Then, we utilize spatial frequency, a gradient-based method to measure sharp variation from these deep features, to reflect activity levels. We apply some consistency verification methods to adjust the decision map and draw out the fused result. Our method analyzes sharp appearances in deep features instead of original images, which can be seen as another success story of unsupervised learning in image processing. Experimental results demonstrate that the proposed method achieves state-of-the-art fusion performance compared to 16 fusion methods in objective and subjective assessments, especially in gradient-based fusion metrics.

Automated summary

The study introduces an unsupervised deep learning model for multi-focus image fusion, achieving state-of-the-art fusion performance in objective and subjective assessments, especially in gradient-based fusion metrics.