Abstract
A novel multi-focus image fusion algorithm based on deep support value convolutional neural network (DSVCNN) is proposed for multi-focus image fusion. First, a deep support value training network is presented by replacing the empirical risk minimization-based loss function by a loss function based on structural risk minimization during the training of convolutional neural network (CNN). Then, to avoid the loss of information, max-pooling/subsampling of the feature mapping layer of a conventional convolutional neural network, which is employed in all conventional CNN frameworks to reduce the dimensionality of the feature map, is replaced by standard convolutional layers with a stride of two. The experimental results demonstrate that the suggested DSVCNN-based method is competitive with current state-of-the-art approaches and superior to those that use traditional CNN methods.
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