Image super-resolution reconstruction based on improved Dirac residual network

In recent years, to improve the nonlinear feature mapping ability of the image super-resolution network, the depth of the convolutional neural network is getting deeper and deeper. In the existing residual network, the the residual block’s output and input are added directly through the skip connection to deepen the nonlinear mapping layer. However, it can not be proved that every addition is useful to improve the network’s performance. In this paper, based on Dirac convolution, an improved Dirac residual block is proposed, which uses the trainable parameters to adaptively control the balance of the convolution and the skip connection to increase the nonlinear mapping ability of the model. The main body network uses multiple Dirac residual blocks to learn the nonlinear mapping of high-frequency information between LR and HR images. In addition, the global skip connection is realized by sub-pixel convolution, which can learn to use linear mapping of low-frequency features of input LR image. In the training stage, the model uses Adam optimizer for network training and L1 as the loss function. The experiments compare our algorithm with some other state-of-the-art models in PSNR, SSIM, IFC, and visual effect on five different benchmark datasets. The results show that the proposed model has excellent performance both in subjective and objective evaluation.