  In this work, we propose a novel unsupervised deep learning model to address
multi-focus image fusion problem. First, we train an encoder-decoder network in
unsupervised manner to acquire deep feature of input images. And then we
utilize these features and spatial frequency to measure activity level and
decision map. Finally, we apply some consistency verification methods to adjust
the decision map and draw out fused result. The key point behind of proposed
method is that only the objects within the depth-of-field (DOF) have sharp
appearance in the photograph while other objects are likely to be blurred. In
contrast to previous works, our method analyzes sharp appearance in deep
feature instead of original image. Experimental results demonstrate that the
proposed method achieves the state-of-art fusion performance compared to
existing 16 fusion methods in objective and subjective assessment.
