A novel image super-resolution reconstruction algorithm based on improved GANs and gradient penalty

作     者：Shuangshuang Liu Xiaoling Li 

作者机构：Department of Computer ScienceNanchang Business College of Jiangxi Agricultural UniversityNanchangChina Department of Information EngineeringGongqing College of Nanchang UniversityGongqingchengChina 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2019年第12卷第3期

页      面：400-413页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Deep model’s feature maps Generative adversarial networks Gradient penalty,Image super-resolution Wasserstein distance 

摘      要：Purpose–Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient *** order to solve such problems,the purposeof this paperis to proposea novel image super-resolutionalgorithmbasedon improved generative adversarial networks(GANs)with Wasserstein distance and gradient ***/methodology/approach–The proposed algorithm first introduces the conventional GANs architecture,the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction(SRWGANs-GP).In addition,a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution *** content loss is extracted from the deep model’s feature maps,and such features are introduced to calculate mean square error(MSE)for the loss calculation of ***–To validate the effectiveness and feasibility of the proposed algorithm,a lot of compared experiments are applied on three common data sets,***5,Set14 and *** results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively *** with the baseline deep models,the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution *** MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and ***/value–Compared with the state-of-the-art algorithms,the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.