Wide receptive field networks for single image super-resolution

Recently, using deep learning(DL) in super-resolution(SR) has ac- hieved great success. These methods combine the convolutional neural network(CNN) to learn a general matrix function for an end-to-end mapping. However, as the width and depth of the network increase, there are two essential problems in the SR tasks. On the one hand, a wider and deeper network will bring better performance but increase the calculating complexity and memory consumption. On the other hand, the expanded architecture will miss the intermediate feature details in the information transmitting process. Hence, a SISR(Single Image Super-Resolution) network with wider feature information blocks(WFIB) is proposed to address these issues by making a balance between the network complexity and performance. Cascade residual block(CRB) helps the network make full use of contextual feature information. Extensive experiments verify that our network achieves better performance and with fewer parameters than the state-of-the-art methods.