| 英文标题 | Single image super resolution based on ResNeXt and WGAN |
| 作者英文名 | Zeng Qingliang, Nan Fangzhe, Shang Diya, Sun Hua |
| 机构英文名 | College of Software,Xinjiang University,Urumqi 830046,China |
| 英文摘要 | To solve the problem of unstable training and slow learning speed problems of a generative adversarial network for image super-resolution(SRGAN), the paper proposed a single image super-resolution reconstruction model called the Res_SRGAN based on ResNeXt and WGAN. The model referred to ResNeXt network structure construction generator, which reduced the computational complexity of model generator to 1/8 that of the SRGAN. The discriminator was constructed by WGAN, which solved SRGAN's instability. Experimental results demonstrate that the proposed model achieves better performance in subjective and objective evaluations using four public data sets compared with other single-image super-resolution reconstruction models. |
| 英文关键词 | single image super-resolution reconstruction; ResNeXt; Wasserstein GANC(WGAN); deep learning |