论文标题
图像denoising的晶格融合网络
Lattice Fusion Networks for Image Denoising
论文作者
论文摘要
本文提出了一种卷积神经网络中特征融合的新方法。建议使用不同的特征融合技术来促进信息流并改善深度神经网络的训练。其中一些技术以及提出的网络可以被视为一种有向的无环图（DAG）网络，其中一层可以从其他层接收输入并具有到其他层的输出。在晶格融合网络（LFNET）的提议的一般框架中，每个卷积层的特征图将基于晶格图结构（节点是卷积层）传递给其他层。为了评估所提出的体系结构的性能，实施了基于LFNET一般框架的不同设计，以实现图像DeNoising的任务。此任务被用作训练深度卷积网络的示例。将结果与最新方法的状态进行比较。所提出的网络能够以更少的可学习参数获得更好的结果，这显示了LFNET在培训深神经网络方面的有效性。
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.
