Recurrent embedded hourglass network for single image super-resolution

Abstract

The single image super-resolution has become an important topic of discussion due to the demand for high-quality digital images in the field of visual artificial intelligence. The deep learning-based approach has achieved great success because of its excellent ability to control complex features. Using simply extending or deepening the network, performance can be improved slightly, but not significantly. There are still blurry edges and rough texture details. In this paper, we propose a Recurrent Embedded Hourglass Network (SRRHN) for super-resolution reconstruction. We use the hourglass network to combine deep and shallow features and embed Gated Recurrent Unit (GRU) in each layer of the hourglass network to improve the long-range correlations. Finally, sub-pixel convolution is adopted to avoid image distortion during upsampling. Extensive experiments on several standard benchmarks show that our proposed method achieves better performance compared with state-of-the-art methods.