Search Results for author: Yihao Liu

Found 15 papers, 7 papers with code

Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning

no code implementations9 Oct 2021 Yihao Liu, Hengyuan Zhao, Kelvin C. K. Chan, Xintao Wang, Chen Change Loy, Yu Qiao, Chao Dong

We address this problem from a new perspective, by jointly considering colorization and temporal consistency in a unified framework.

Colorization

Learn to Match: Automatic Matching Network Design for Visual Tracking

1 code implementation ICCV 2021 Zhipeng Zhang, Yihao Liu, Xiao Wang, Bing Li, Weiming Hu

Siamese tracking has achieved groundbreaking performance in recent years, where the essence is the efficient matching operator cross-correlation and its variants.

Visual Tracking

RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank

no code implementations20 Jul 2021 Wenlong Zhang, Yihao Liu, Chao Dong, Yu Qiao

To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics.

Image Super-Resolution Learning-To-Rank

Blind Image Super-Resolution: A Survey and Beyond

no code implementations7 Jul 2021 Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, Chao Dong

This paper serves as a systematic review on recent progress in blind image SR, and proposes a taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used for solving the SR model.

Image Super-Resolution

Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings

3 code implementations15 Jun 2021 Hengyuan Zhao, Wenhao Wu, Yihao Liu, Dongliang He

In this paper, we present a fast exemplar-based image colorization approach using color embeddings named Color2Embed.

Colorization Semantic correspondence

HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization

1 code implementation27 May 2021 Xiangyu Chen, Yihao Liu, Zhengwen Zhang, Yu Qiao, Chao Dong

In this work, we propose a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction with denoising and dequantization.

Denoising HDR Reconstruction +1

Very Lightweight Photo Retouching Network with Conditional Sequential Modulation

no code implementations13 Apr 2021 Yihao Liu, Jingwen He, Xiangyu Chen, Zhengwen Zhang, Hengyuan Zhao, Chao Dong, Yu Qiao

Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects such as poor contrast, over/under exposure, and inharmonious saturation.

Photo Retouching

Conditional Sequential Modulation for Efficient Global Image Retouching

1 code implementation ECCV 2020 Jingwen He, Yihao Liu, Yu Qiao, Chao Dong

The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector.

Photo Retouching

Enhanced Quadratic Video Interpolation

2 code implementations10 Sep 2020 Yihao Liu, Liangbin Xie, Li Si-Yao, Wenxiu Sun, Yu Qiao, Chao Dong

In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model.

Super-Resolution Video Frame Interpolation

FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing

no code implementations20 Jan 2020 Yu Dong, Yihao Liu, He Zhang, Shifeng Chen, Yu Qiao

With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts.

Image Dehazing Single Image Dehazing

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution

1 code implementation ICCV 2019 Wenlong Zhang, Yihao Liu, Chao Dong, Yu Qiao

To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of perceptual metrics.

Image Super-Resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

28 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 +1

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