Improved Image Super-resolution Algorithm for Generating Adversarial Network

Although SRGAN algorithm has many advantages, it also has some problems such as low reconstruction effect, large number of parameters and poor performance of activation function. Therefore, an image super-resolution algorithm SRGAN-E based on SRGAN is proposed in this paper. Firstly, BN layer is deleted to improve the reconstruction effect. Furthermore, a one-dimensional convolutional attention mechanism was added to the original generator model to make the image pay more attention to the context information and reduce the generator parameters in the network model. To improve the performance of the discriminator, the LeakyReLU function in the discriminator model of SRGAN algorithm was changed to Mish function. The experimental results show that compared with SRGAN algorithm, the average value of PSNR and SSIM of SRGAN-E algorithm are increased by 0.345 and 0.009 respectively on four test sets. The number of generator parameters of SRGAN-E algorithm is reduced by 1 388 compared with SRGAN algorithm. The SRGAN-E algorithm not only improves the image reconstruction effect but also reduces the model parameters.