Comparative Analysis the Super-Resolution Image Generation Performance Based on BigGAN and VQ-VAE-2

Abstract

Super-resolution image reconstruction has always been a popular research direction in the field of computer vision, which aims to recover high-resolution clear images from low-resolution images. Traditional super-resolution reconstruction algorithms mainly rely on the construction of constraints and the accuracy of registration between images to achieve the reconstruction effect, but their accuracy cannot meet the needs of practical applications with large multiples. Thanks to the rapid development of deep learning field, super-resolution image reconstruction based on deep learning has become the mainstream, and has achieved great success in reconstruction accuracy and speed. According to the different generative models used, the existing super-resolution image reconstruction methods mainly include two categories: GAN-based and VAEs-based. To quantitatively compare the limits of the two approaches' performance, this study selects two representative algorithms, BigGAN and VQ-VAE-2, and introduces the theoretical details and training process of these two methods, respectively. Furthermore, the reconstruction results of BigGAN and VQ-VAE-2 are further compared. Finally, this paper discusses the future development trend of super-resolution picture reconstruction with the current potential problems of BigGAN and VQ-VAE-2.