Residual aggregation U-shaped network for image super-resolution.

Recent research on image super-resolution (SR) task has greatly progressed with the development of convolutional neural networks (CNNs). Most previous studies with single-scale feature enhance expressiveness by increasing network depth. However, most of them do not adequately extract and utilize multi-scale features. In this paper, we propose a novel residual aggregation U-shaped network (RAU), which fully utilizes multi-scale features to help reconstruct high-quality images. First, we use progressive downsampling structure to obtain multi-scale features and capture context information, and use progressive upsampling structure to fuse multi-scale features and fill detail texture. Second, we introduce auxiliary supervision in the middle layer to provide additional regularization and accelerate the convergence speed. Third, we propose a lightweight model for our network, and we replace the traditional convolution with the Ghost module in multiple locations of network. Extensive experiments on the challenge datasets confirmed the effectiveness of the proposed network. Our algorithm can restore high-quality high-resolution (HR) images and outperform other methods by a large margin. [ABSTRACT FROM AUTHOR]