High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets


  • Yanbo Wang East China Normal University
  • Chuming Lin Tencent YouTu Lab
  • Donghao Luo Tencent YouTu Lab
  • Ying Tai Tencent YouTu Lab
  • Zhizhong Zhang East China Normal University
  • Yuan Xie East China Normal University




CV: Computational Photography, Image & Video Synthesis, CV: Low Level & Physics-Based Vision


The last decades are marked by massive and diverse image data, which shows increasingly high resolution and quality. However, some images we obtained may be corrupted, affecting the perception and the application of downstream tasks. A generic method for generating a high-quality image from the degraded one is in demand. In this paper, we present a novel GAN inversion framework that utilizes the powerful generative ability of StyleGAN-XL for this problem. To ease the inversion challenge with StyleGAN-XL, Clustering \& Regularize Inversion (CRI) is proposed. Specifically, the latent space is firstly divided into finer-grained sub-spaces by clustering. Instead of initializing the inversion with the average latent vector, we approximate a centroid latent vector from the clusters, which generates an image close to the input image. Then, an offset with a regularization term is introduced to keep the inverted latent vector within a certain range. We validate our CRI scheme on multiple restoration tasks (i.e., inpainting, colorization, and super-resolution) of complex natural images, and show preferable quantitative and qualitative results. We further demonstrate our technique is robust in terms of data and different GAN models. To our best knowledge, we are the first to adopt StyleGAN-XL for generating high-quality natural images from diverse degraded inputs. Code is available at https://github.com/Booooooooooo/CRI.




How to Cite

Wang, Y., Lin, C., Luo, D., Tai, Y., Zhang, Z., & Xie, Y. (2023). High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets. Proceedings of the AAAI Conference on Artificial Intelligence, 37(3), 2716-2723. https://doi.org/10.1609/aaai.v37i3.25371



AAAI Technical Track on Computer Vision III