Search Results for author: Kelvin C. K. Chan

Found 14 papers, 12 papers with code

Reference-based Image and Video Super-Resolution via C2-Matching

1 code implementation19 Dec 2022 Yuming Jiang, Kelvin C. K. Chan, Xintao Wang, Chen Change Loy, Ziwei Liu

To tackle these challenges, we propose C2-Matching in this work, which performs explicit robust matching crossing transformation and resolution.

Image Super-Resolution Reference-based Super-Resolution +2

GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond

1 code implementation29 Jul 2022 Kelvin C. K. Chan, Xiangyu Xu, Xintao Wang, Jinwei Gu, Chen Change Loy

While most existing perceptual-oriented approaches attempt to generate realistic outputs through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN.

Colorization Image Colorization +2

Exploring CLIP for Assessing the Look and Feel of Images

1 code implementation25 Jul 2022 Jianyi Wang, Kelvin C. K. Chan, Chen Change Loy

Measuring the perception of visual content is a long-standing problem in computer vision.

Image Quality Assessment

Towards Robust Blind Face Restoration with Codebook Lookup Transformer

1 code implementation22 Jun 2022 Shangchen Zhou, Kelvin C. K. Chan, Chongyi Li, Chen Change Loy

In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.

Blind Face Restoration

On the Generalization of BasicVSR++ to Video Deblurring and Denoising

1 code implementation11 Apr 2022 Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy

The exploitation of long-term information has been a long-standing problem in video restoration.

Deblurring Denoising +2

Investigating Tradeoffs in Real-World Video Super-Resolution

1 code implementation CVPR 2022 Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy

The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training.

Video 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).

Reference-based Super-Resolution

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

3 code implementations CVPR 2022 Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy

We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively.

Video Enhancement Video Restoration +1

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).

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

11 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.

Deblurring Video Enhancement +2

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