Densely connected network with improved pyramidal bottleneck residual units for super-resolution

Abstract

Recent studies have shown that super-resolution can be significantly improved by using deep convolution neural network. Although applying a larger number of convolution kernels can extract more features, increasing the number of feature mappings will dramatically increase the training parameters and time complexity. In order to balance the workload among all units and maintain appropriate time complexity, this paper proposes a new network structure for super-resolution. For the sake of making full use of context information, in the structure, the operations of division (S) and fusion (C) are added to the pyramidal bottleneck residual units, and the dense connected methods are used. The proposed network include a preliminary feature extraction net, seven residual units with dense connections, seven convolution layers with the size of 1×1 after each residual unit, and a deconvolution layer. The experimental results show that the proposed network has better performance than most existing methods.