Image super-resolution using convolutional neural network with symmetric skip connections

Abstract

In recent years, the convolution neural network has been widely used in single image super-resolution and has an excellent super-resolution ability. In this paper, a novel convolutional neural network structure based on symmetric skip connection is proposed, which contains multiple convolution layers and deconvolution layers. The role of the convolution layer is to extract the details of image content, and the function of the deconvolution layer is to make the image upsampling and restore the image content details. In addition, we use skip connection between the convolution layer and the deconvolution layer of network structure, which can transfer image information from the front end to the back end. Meanwhile, skip connection can also effectively solve the problem of gradient vanishing. Besides, the residual block is introduced to deepen the network structure. The deeper network structure can learn more complex changes. Different from other papers, this paper uses the method of adding the number of channels for feature fusion. This method can greatly increase the number of feature images, which is helpful to restore image details by deconvolution layer. A large number of experiments show that our network has efficient super-resolution ability of infrared image details.