Search Results for author: Xintao Wang

Found 13 papers, 8 papers with code

Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution

no code implementations2 Aug 2021 Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, Ying Shan

Unlike previous integral gradient methods, our FAIG aims at finding the most discriminative filters instead of input pixels/features for degradation removal in blind SR networks.

Super-Resolution

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

1 code implementation22 Jul 2021 Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan

Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.

Super-Resolution

Robust Reference-based Super-Resolution via C2-Matching

1 code implementation CVPR 2021 Yuming Jiang, Kelvin C. K. Chan, Xintao Wang, Chen Change Loy, Ziwei Liu

However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e. g. scale and rotation) and the resolution gap (e. g. HR and LR).

Super-Resolution

Towards Real-World Blind Face Restoration with Generative Facial Prior

1 code implementation CVPR 2021 Xintao Wang, Yu Li, Honglun Zhang, Ying Shan

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details.

Blind Face Restoration GAN inversion

Positional Encoding as Spatial Inductive Bias in GANs

no code implementations CVPR 2021 Rui Xu, Xintao Wang, Kai Chen, Bolei Zhou, Chen Change Loy

In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators.

Image Manipulation

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

4 code implementations CVPR 2021 Kelvin C. K. Chan, Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy

Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension.

Video Super-Resolution

GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution

no code implementations CVPR 2021 Kelvin C. K. Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, Chen Change Loy

We show that pre-trained Generative Adversarial Networks (GANs), e. g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR).

GAN inversion Image Super-Resolution

Understanding Deformable Alignment in Video Super-Resolution

no code implementations15 Sep 2020 Kelvin C. K. Chan, Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy

Aside from the contributions to deformable alignment, our formulation inspires a more flexible approach to introduce offset diversity to flow-based alignment, improving its performance.

Optical Flow Estimation Video Super-Resolution

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

7 code implementations7 May 2019 Xintao Wang, Kelvin C. K. Chan, Ke Yu, Chao Dong, Chen Change Loy

In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.

 Ranked #1 on Deblurring on REDS

Deblurring Video Restoration +1

Path-Restore: Learning Network Path Selection for Image Restoration

no code implementations23 Apr 2019 Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, Chen Change Loy

To leverage this, we propose Path-Restore, a multi-path CNN with a pathfinder that can dynamically select an appropriate route for each image region.

Denoising Image Restoration

Deep Network Interpolation for Continuous Imagery Effect Transition

2 code implementations CVPR 2019 Xintao Wang, Ke Yu, Chao Dong, Xiaoou Tang, Chen Change Loy

Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect.

Image Restoration Image-to-Image Translation +1

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

27 code implementations1 Sep 2018 Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang

To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).

Face Hallucination Image Super-Resolution

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