Abstract: The process of using low resolution (LR) images to predict the corresponding high resolution (HR) images is called image super-resolutions, which aims to get a clear image. With the vigorous development of artificial intelligence, image super-resolution reconstruction has more and more applications in many fields such as medical treatment and remote sensing. This article learns to use the deep learning model of generative adversarial networks (GAN) to perform single-image super-reconstruction. Compared with traditional methods, this model proposes a new perceptual loss function, including an adversarial loss and a content loss. The adversarial loss distinguishes the generated image from the actual high-resolution image by training the discriminator network structure, while content loss uses the pre-trained VGG19 network model to calculate the perceived similarity of image features, rather than the similarity in pixel space. The experiment proves that the introduction of generative adversarial network into single-image super-resolution, MOS score is higher than traditional methods. This article will focus on the principles, effects, and applications of SR and GAN.

Key words: single image super-resolution, generative adversarial networks, VGG19 networks, content loss function, perceptual loss function