稳定增强生成对抗网络在壁画的超分辨率重建  

Murals Super-resolution Reconstruction with the Stable Enhanced Generative Adversarial Network

作　　者：曹建芳 贾一鸣[1] 闫敏敏 田晓东 Cao Jianfang;Jia Yiming;Yan Minmin;Tian Xiaodong(College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China;Department of Computer,Xinzhou Teachers University,Xinzhou 034000,China)

机构地区：[1]太原科技大学计算机科学与技术学院,山西太原030024 [2]忻州师范学院计算机系,山西忻州034000

出　　处：《系统仿真学报》2022年第5期1076-1089,共14页Journal of System Simulation

基　　金：山西省高等学校人文社会科学重点研究基地项目(20190130)。

摘　　要：针对古代壁画分辨率低、纹理细节模糊不清导致壁画观赏性不足和研究价值不高的问题,提出了一种稳定增强生成对抗网络的超分辨率重建算法(stableenhancedsuper-resolutiongenerative adversarial networks,SESRGAN)。以生成对抗网络为基础框架,生成网络采用密集残差块提取壁画特征,使用VGG(visual geometry group)网络作为判别网络的基本框架判断输入壁画的真假,引入感知损失、内容损失和惩罚损失三个损失共同优化模型。实验结果表明,与其他相关的超分辨率算法进行比较,峰值信噪比平均提高了0.4~2.62dB,结构相似性提高了0.013~0.027,主观感知评估也有提高。Aiming at the problems of low resolution and unclear texture details of ancient murals,which led to insufficient viewing of murals and low research value,a stable enhanced super-resolution generative adversarial networks(SESRGAN)reconstruction algorithm is proposed.Based on the generative adversarial network,the generative network uses dense residual blocks to extract mural features,and uses the visual geometry group(VGG)network as the basic framework of the discriminating network to determine the authenticity of the input mural,and introduces perception loss,content loss and penalty loss to jointly optimize the model.Experimental results show that,compared with other related super-resolution algorithms,the peak signal-to-noise ratio(PSNR)is improved by 0.4~2.62 dB on average,the structural similarity is improved by 0.013~0.027,and the subjective perception evaluation is also improved.

关 键 词：古代壁画 超分辨率重建 生成对抗网络 密集残差块 惩罚损失 

分 类 号：TP391.47[自动化与计算机技术—计算机应用技术]