Single Image Super-Resolution Reconstruction based on the ResNeXt Network

To solve the complex computation, unstable network and slow learning speed problems of a generative adversarial network for image super-resolution (SRGAN), we proposed a single image super-resolution reconstruction model called the Res_WGAN based on ResNeXt. The generator is constructed by the ResNeXt network, which reduced the computational complexity of the model generator to 1/8 that of the SRGAN. The discriminator was constructed by the Wasserstein GAN(WGAN), which solved the SRGAN's instability. By removing the normalization operation in the residual network, the learning rate is improved. The experimental results from the Res_WGAN demonstrated that the proposed model achieved better performance in the subjective and objective evaluations using four public data sets compared with other state-of-the-art models.