At present, generative adversarial network (GAN) has made outstanding progress for image super-resolution (SR), solving the problem of edge smoothing in SR images. Nevertheless, there are also problems with a lack of texture details, noise, and artifacts. The high-frequency (HF) information of an image is significant for keeping the texture part of the reconstructed image, and the introduction of HF information can increase the learning ability of super-resolution generative adversarial network (SRGAN) for the image features, so we propose a super-resolution GAN based on high-frequency feature fusion (HFF-SRGAN), which can increase the texture detail of the reconstructed image and reduce the noise and artifacts with less computational cost. Experimental results show that our algorithm evaluates significantly better than SRGAN and has significant advantages in texture details and noise compared with other SR algorithms.