Critical review on deep learning and smart technologies for image super-resolution

Abstract

Image super-resolution is an extremely useful way to improve the quality of an image. It is miracle that making use of the current signal processing and deep learning technologies, the image can look much appealing after the super-solution. This review paper is to highlight important techniques, especially to point out recent key contributions to make superior success of super-resolution of the recent years, especially on face super-resolution. We will start with a very brief and quick review on using conventional signal processing and classic learning approaches for super-resolution, and then concentrate on giving the advantages of deep learning, in particular, the recent powerful concepts on using latent vector and facial priors to achieve superior performance. Further topics of discussion include generative adversarial network (GAN), StyleGAN, latent space, facial priors and diffusion models. Our concentration is on the reasons for the success of these techniques. Attractive demonstrations on a few state-of-the-art models, including some of our work, are provided.