Multi-focus image fusion using deep support value convolutional neural network

• Published in: Optik (Stuttgart) Vol. 176; pp. 567 - 578
• Main Authors: Du, ChaoBen, Gao, SheSheng, Liu, Ying, Gao, BingBing
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.01.2019
• Subjects: Convolutional neural network; Decision map; Image fusion; Multi-focus image; Decision map; Multi-focus image; Convolutional neural network; Image fusion
• ISSN: 0030-4026; 1618-1336
• DOI: 10.1016/j.ijleo.2018.09.089

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.