Search Results for author: Jinjin Gu

Found 17 papers, 7 papers with code

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

Attention in Attention Network for Image Super-Resolution

2 code implementations19 Apr 2021 Haoyu Chen, Jinjin Gu, Zhi Zhang

In this work, we attempt to quantify and visualize attention mechanisms in SISR and show that not all attention modules are equally beneficial.

Image Super-Resolution

Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric

no code implementations30 Nov 2020 Jinjin Gu, Haoming Cai, Haoyu Chen, Xiaoxing Ye, Jimmy Ren, Chao Dong

To answer the questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing ALgorithms (PIPAL) dataset.

Image Quality Assessment Image Restoration

Interpreting Super-Resolution Networks with Local Attribution Maps

no code implementations CVPR 2021 Jinjin Gu, Chao Dong

Based on LAM, we show that: (1) SR networks with a wider range of involved input pixels could achieve better performance.

Image Super-Resolution

PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration

no code implementations ECCV 2020 Jinjin Gu, Haoming Cai, Haoyu Chen, Xiaoxing Ye, Jimmy Ren, Chao Dong

To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset.

Image Quality Assessment Image Restoration +1

Image Processing Using Multi-Code GAN Prior

1 code implementation CVPR 2020 Jinjin Gu, Yujun Shen, Bolei Zhou

Such an over-parameterization of the latent space significantly improves the image reconstruction quality, outperforming existing competitors.

Blind Face Restoration Colorization +5

Interpreting the Latent Space of GANs for Semantic Face Editing

4 code implementations CVPR 2020 Yujun Shen, Jinjin Gu, Xiaoou Tang, Bolei Zhou

In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.

Face Generation GAN inversion +1

Suppressing Model Overfitting for Image Super-Resolution Networks

no code implementations11 Jun 2019 Ruicheng Feng, Jinjin Gu, Yu Qiao, Chao Dong

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved.

Image Super-Resolution

Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution

1 code implementation7 May 2019 Guocheng Qian, Yuanhao Wang, Chao Dong, Jimmy S. Ren, Wolfgang Heidrich, Bernard Ghanem, Jinjin Gu

Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM $\to$ DN $\to$ SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality.

Demosaicking Denoising +1

Blind Super-Resolution With Iterative Kernel Correction

2 code implementations CVPR 2019 Jinjin Gu, Hannan Lu, WangMeng Zuo, Chao Dong

In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown.

Super-Resolution

Two-phase Hair Image Synthesis by Self-Enhancing Generative Model

no code implementations28 Feb 2019 Haonan Qiu, Chuan Wang, Hang Zhu, Xiangyu Zhu, Jinjin Gu, Xiaoguang Han

Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).

Image-to-Image Translation Super-Resolution +1

Super-Resolution Perception for Industrial Sensor Data

no code implementations6 Sep 2018 Jinjin Gu, Haoyu Chen, Guolong Liu, Gaoqi Liang, Xinlei Wang, Junhua Zhao

In this paper, we present the problem formulation and methodology framework of Super-Resolution Perception (SRP) on industrial sensor data.

Fault Detection Super-Resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

29 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

Single Image Reflection Removal Using Deep Encoder-Decoder Network

3 code implementations31 Jan 2018 Zhixiang Chi, Xiaolin Wu, Xiao Shu, Jinjin Gu

Image of a scene captured through a piece of transparent and reflective material, such as glass, is often spoiled by a superimposed layer of reflection image.

Reflection Removal

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