RTEN-SR: A reference-based texture enhancement network for single image super-resolution

Abstract

Most current super-resolution (SR) reconstruction methods suffer from edge blurring and insufficient detail reconstruction. To avoid these problems, this paper proposes a reference-based texture enhancement network for single image SR (RTEN-SR). Firstly, a preliminary reconstruction module (PRM) is constructed to learn the initial reconstructed high-resolution (HR) image features. Then, a multi-scale texture enhancement module (MTEM) based on the idea of texture transfer is designed to further supplement and enhance the texture details for the initial reconstructed HR images. To improve the feature learning ability of network, an efficient mixed attention module (EMAM) is constructed by combining channel attention and spatial attention. Meanwhile, the EMAM is integrated into the above two modules to enhance the important image features in the channel and spatial dimensions. Experimental results demonstrate that the proposed method can achieve better performance than state-of-the-art methods, effectively improving the objective indexes and visual results of reference-based image super-resolution (RefSR). For example, the proposed method outperforms the best RefSR method among the comparison methods by 0.38 dB in PSNR metric and 0.128 in SSIM metric.