LADDER PYRAMID NETWORKS FOR SINGLE IMAGE SUPER-RESOLUTION

Benefiting from the powerful representation capability of convolutional neural networks, the performance of single image super-resolution (SISR) has been substantially improved in recent years. However, many current CNN-based methods are computation-intensive because of large-size intermediate feature maps and inefficient convolutions. To resolve these problems, we propose Ladder Pyramid Network (LPN) for single image super-resolution. Firstly, we use strided convolution to reduce the size of the intermediate feature maps and thus reducing computation burden. In order to better balance the effectiveness and efficiency, we propose Ladder Pyramid Module to gradually fuse hierarchical features to enhance performance. Secondly, lightweight convolution block similar to Inverted Residual Module of Mobilenet-v2 was introduced into SISR, with which we build the network backbone and ladder feature pyramid. Experimental results demonstrate that the proposed Ladder Pyramid Network can achieve comparable or better performance than previous lightweight networks while reducing the amount of computation.