IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 2021

A Two-Stage Attentive Network for Single Image Super-Resolution

Jiqing Zhang¹ Chengjiang Long^2,* Yuxin Wang^1,* Haiyin Piao³ Haiyang Mei¹ Xin Yang^1,* Baocai Yin¹

¹ Dalian University of Technology ² JD Finance America Corporation ³ Northwestern Polytechnical University

Contact us: xinyang@dlut.edu.cn jqz@mail.dlut.edu.cn mhy666@mail.dlut.edu.cn

Abstract

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image superresolution (SISR) and contribute remarkable progress. However, most of the existing CNNs-based SISR methods do not adequately explore contextual information in the feature extraction stage and pay little attention to the final high-resolution (HR) image reconstruction step, hence hindering the desired SR performance. To address the above two issues, in this paper, we propose a twostage attentive network (TSAN) for accurate SISR in a coarse-tofine manner. Specifically, we design a novel multi-context attentive block (MCAB) to make the network focus on more informative contextual features. Moreover, we present an essential refined attention block (RAB) which could explore useful cues in HR space for reconstructing fine-detailed HR image. Extensive evaluations on four benchmark datasets demonstrate the efficacy of our proposed TSAN in terms of quantitative metrics and visual effects.

Visual Results

Downloads

Paper	: [ TSAN.pdf ]
Experimental results	: [ Results.zip ]
Pre-trained model.	: [ TSAN.pth ]
Source Code.	: [ Code ]

BibTex

@article{Zhang_2021_TCSVT,
    author = {Zhang, Jiqing and Long, Chengjiang and Wang, Yuxin and Piao, Haiyin and Mei, Haiyang and Yang, Xin and Yin, Baocai},
    title = {A Two-Stage Attentive Network for Single Image Super-Resolution},
    booktitle = {IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)},
    year = {2021}
}