Dual-Path Recurrent Network for Image Super-Resolution

Recently, significant progress has been made for the task of image super-resolution (SR). However, existing methods show that stacking convolution layers blindly leads to overwhelming parameters and high computational complexities. Besides, the conventional feed-forward architectures can hardly fully exploit the mutual dependencies between low- and high-resolution images. Motivated by these observations, we first propose a novel architecture by taking advantage of recursive learning. Based on dual-path block (DPB), we enhance the network performance simply by increasing the number of DPB recursions and avoiding additional parameters. Moreover, in contrast to most convolutional neural network (CNN) methods with one state, our network is endowed with two states (low-resolution state and high-resolution state) transformed mutually. We exploit the relationship between the two states by introducing a back-projection operation which calculates the differences between the two states for a better result. Extensive experiments show that our method outperforms the state-of-the-art methods.