Multi-scale channel network based on filer pruning for image super-resolution

Abstract

Image super-resolution is an important image restoration technology, which is widely used in optical system, optical communication and other fields. Deep convolutional neural networks (CNNs) have made a great breakthrough in accuracy of single image super-resolution. Nevertheless, as the depth and width of the networks increase, CNNs methods face the challenge of memory consumption and computational complexity. To solve this problem, in this paper, we introduce a multi-scale channel network (MSCN) based on filter pruning to reconstruct the high-resolution (HR) image from the original low-resolution (LR) image. Firstly, we propose a multi-scale channel block (MSCB) to obtain the fine features based on the convolution kernels with different receptive fields. Channel split operator is used in MSCB to reduce the model parameters. The wider MSCN with more filters is trained to achieve a better performance. Then, we calculate the rank of feature maps and prune filters with low-rank feature information. Finally, the compressed multi-scale channel network with fewer parameters can achieve similar accuracies. The experimental results validate the effectiveness of our approaches.