Channel rearrangement multi-branch network for image super-resolution

Abstract

In image super-resolution task, the existing convolutional neural network methods proceed to increase the number of network layers and filters to achieve better performance. Nevertheless, such approaches not only lead to a dramatic increase in network parameters, but also restrict the practical application of super-resolution methods. In order to address this problem, we propose a lightweight model called Channel Rearrangement Multi-Branch Network (CRMBN) for super-resolution of single images. Specifically, we design a channel rearrangement algorithm based on the total variation of the feature maps. It is inspired by the fact that the feature maps with high total variation contain more high-frequency details, which are valuable for super-resolution reconstruction. Therefore, for the super-resolution networks, the more feature maps with high total variation, the better performance of reconstruction. The rearranged channels are divided into two groups, one with high total variation and the other with the opposite. In order to reduce the parameters while mining for more details, we send the features with low total variation to the deep network. Finally, high total variation features and deep features are fused to reconstruct high-resolution image. The experimental results show that compared with other lightweight models, the proposed method can offer a favorable improvement in terms of PSNR and SSIM.