摘要
为了解决生成对抗网络(Generative adversarial network,GAN)训练不稳定问题,降低模型复杂度,加快网络学习速率,提高超分辨率图像的视觉效果和重建速率,提出了一种基于改进生成对抗网络的图像超分辨率方法。该方法以改进的生成对抗网络为模型,通过粗粒度主体内容和细粒度细节边缘结合的方式提取图像特征,利用线性组合的方式重建超分辨率图像,采用Wasserstein距离优化生成对抗网络。实验结果表明:该方法能够生成视觉效果良好的超分辨率图像,在Set5、Set14等测试集上,其主观视觉评价和客观量化指标(PSNR、SSIM)都优于SRGAN方法。该方法通过重新设计网络模型,使得特征提取更为全面,网络训练更加充分,有助于提高超分辨率图像重建速度,提高图像质量。
To solve the problem of training instability of generative adversarial network,reduce model complexity,and speed up network learning rate,and improve the visual effect and reconstruction rate of super-resolution image,an image super-resolution method based on improved generative adversarial networks is proposed.In the method,improved generative adversarial network is taken as the model,image features are extracted by combining main content of coarse granularity with detail edge of fine granularity,super-resolution images are reconstructed by means of linear combination,and generative adversarial network is optimized via Wasserstein distance.Experimental results show that super-resolution images with advanced visual effect can be generated with this method,and the method is superior to SRGAN in respect of subjective evaluation and objective quantification(PSNR/SSIM)in Set5,Set14 and such test sets.With this method,by redesigning the network model,feature extraction is conducted more comprehensively,and network training is conducted more completely,which helps to improve the speed of super-resolution image reconstruction and image quality.
作者
包晓安
高春波
张娜
徐璐
吴彪
BAO Xiaoan;GAO Chunbo;HANG Na;XU Lu;WU Biao(School of Information Science and Technology ,Zhejiang Sci-Tech University,Hangzhou 310018,China;Department of East Asian Studies,Yamaguchi University,Yamaguchi 753-8514,Japan)
出处
《浙江理工大学学报（自然科学版）》
2019年第4期499-508,共10页
Journal of Zhejiang Sci-Tech University(Natural Sciences)
基金
国家自然科学基金项目(61502430,61562015)
广西自然科学重点基金项目(2015GXNSFDA139038)
浙江理工大学521人才培养计划
