Fast Single Image Super-Resolution Via Dilated Residual Networks

Recently, deep convolutional neural networks (CNNs) have been attracting considerable attention in single image super-resolution. Some CNN-based methods, such as VDSR verified that residual learning can speed up the training and significantly improve the performance of accuracy. However, with very deep networks, convergence speed is still a critical issue in training due to the cost of requiring enormous parameters. In order to deal with this issue, we redesign the residual networks based on dilated networks. In this paper, we propose symmetrical dilated residual convolution networks (FDSR) to tackle image super-resolution problems. Our network is on the basis of the dilated convolutions supported exponential expansion of the receptive field without loss of resolution and coverage. This means that FDSR can speed up the training and improve the performance of accuracy without increasing the model's depth or complexity. Meanwhile, we attempt to combine the image pre-processing approach of VGG-net with mean squared error (MSE) to enhance the performance. The experimental results demonstrate that the training time-consuming proposed model achieves nearly a half with even superior restoration quality. Further, we present a novel network with less layers and parameters can achieve real-time performance on a generic CPU and still maintain superior performance.