  A novel method for feature fusion in convolutional neural networks is
proposed in this paper. Different feature fusion techniques are suggested to
facilitate the flow of information and improve the training of deep neural
networks. Some of these techniques as well as the proposed network can be
considered a type of Directed Acyclic Graph (DAG) Network, where a layer can
receive inputs from other layers and have outputs to other layers. In the
proposed general framework of Lattice Fusion Network (LFNet), feature maps of
each convolutional layer are passed to other layers based on a lattice graph
structure, where nodes are convolutional layers. To evaluate the performance of
the proposed architecture, different designs based on the general framework of
LFNet are implemented for the task of image denoising. This task is used as an
example where training deep convolutional networks is needed. Results are
compared with state of the art methods. The proposed network is able to achieve
better results with far fewer learnable parameters, which shows the
effectiveness of LFNets for training of deep neural networks.
