  Traditional and deep learning-based fusion methods generated the intermediate
decision map to obtain the fusion image through a series of post-processing
procedures. However, the fusion results generated by these methods are easy to
lose some source image details or results in artifacts. Inspired by the image
reconstruction techniques based on deep learning, we propose a multi-focus
image fusion network framework without any post-processing to solve these
problems in the end-to-end and supervised learning way. To sufficiently train
the fusion model, we have generated a large-scale multi-focus image dataset
with ground-truth fusion images. What's more, to obtain a more informative
fusion image, we further designed a novel fusion strategy based on unity fusion
attention, which is composed of a channel attention module and a spatial
attention module. Specifically, the proposed fusion approach mainly comprises
three key components: feature extraction, feature fusion and image
reconstruction. We firstly utilize seven convolutional blocks to extract the
image features from source images. Then, the extracted convolutional features
are fused by the proposed fusion strategy in the feature fusion layer. Finally,
the fused image features are reconstructed by four convolutional blocks.
Experimental results demonstrate that the proposed approach for multi-focus
image fusion achieves remarkable fusion performance compared to 19
state-of-the-art fusion methods.
