6 citations · 6 across the 5 of their papers we have counts for
5 papers
GAN Inversion for Image Editing via Unsupervised Domain Adaptation
Siyu Xing, Chen Gong, Hewei Guo +3
Existing GAN inversion methods work brilliantly in reconstructing high-quality (HQ) images while struggling with more common low-quality (LQ) inputs in practical application. To ad…
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network
Xiang Wang, Yimin Yang, Zhichang Guo +4
Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perfor…
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network
Xiang Wang, Yimin Yang, Qixiang Pang +3
Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this pape…
DiffGAR: Model-Agnostic Restoration from Generative Artifacts Using Image-to-Image Diffusion Models
Yueqin Yin, Lianghua Huang, Yu Liu +1
Recent generative models show impressive results in photo-realistic image generation. However, artifacts often inevitably appear in the generated results, leading to downgraded use…
Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior
Kai Hu, Yu Liu, Renhe Liu +3
Facial semantic guidance (including facial landmarks, facial heatmaps, and facial parsing maps) and facial generative adversarial networks (GAN) prior have been widely used in blin…