Single Image Super-Resolution Using Dual-Branch Convolutional Neural Network

Recent advances in convolutional neural networks have demonstrated impressive reconstruction for single image super-resolution (SR). However, most of the existing methods are to achieve better performance by directly stacking more convolution layers in a chain way. As the depth increases, it not only lacks time efficiency but also consumes more computer memory. In this paper, we propose a low-complexity dual-branch network, termed DBCN, to deal with the SR problem. The feature extraction part of the proposed DBCN mainly consists of two parallel sub-networks, one extracts fine local information via standard convolution layers, and the other focuses on more contextual information in larger regions by using dilated convolution layers. In addition, deconvolution layers are integrated into the network after feature extraction stage to accelerate the upscaling process. Specifically, a novel skip connection is proposed to learn residual mapping and ease the training. Extensive evaluations on benchmark datasets show the effectiveness of our algorithm, achieving comparable results in terms of accuracy and visual quality compared with several state-of-the-art methods.