中圖分類(lèi)號(hào)： TP183；TP391 文獻(xiàn)標(biāo)識(shí)碼： A DOI： 10.19358/j.issn.2096-5133.2022.01.009 引用格式： 趙志博，滕奇志，任超，等. 基于生成對(duì)抗網(wǎng)絡(luò)的無(wú)監(jiān)督圖像超分辨率算法[J].信息技術(shù)與網(wǎng)絡(luò)安全，2022，41(1)：55-62.
Unsupervised image super-resolution algorithm based on Generative Adversarial Network
Zhao Zhibo，Teng Qizhi，Ren Chao，He Xiaohai，Zhai Sen
(College of Electronics and Information Engineering，Sichuan University，Chengdu 610065，China)
Abstract： In most existing researches on learning-based image super-resolution, the pair of training datasets is generated by down-scaling high-resolution(HR) images through a predetermined operation(e.g.，bicubic down-sampling). However, these algorithms cannot be effectively applied to real scenes since the real-world image contains unknown noise and blur. To this end, we propose an unsupervised image super-resolution algorithm based on Generative Adversarial Network in this paper. Our method contains two parts: domain conversion sub-network and reconstruction sub-network. In addition, the deep feature extraction module is proposed to improve the performance of the network by merging the image features captured by different receptive fields. Extensive experiments illustrate that compared with most current image super-resolution algorithms, the proposed method can be applied to real-world image (containing noise, blur, etc.) super-resolution, and achieves the start-of-the-art(SOTA) performance on both subjective and objective evaluations.
Key words : real-world image super-resolution；domain conversion；Generative Adversarial Network；unsupervised training
