Search Results for author: Xueyang Fu

Found 27 papers, 3 papers with code

Exposure Normalization and Compensation for Multiple-Exposure Correction

no code implementations CVPR 2022 Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, Zhiwei Xiong

However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance.

Image Enhancement

Mutual Information-Driven Pan-Sharpening

no code implementations CVPR 2022 Man Zhou, Keyu Yan, Jie Huang, Zihe Yang, Xueyang Fu, Feng Zhao

Despite the remarkable progress, existing state-of-the-art Pan-sharpening methods don't explicitly enforce the complementary information learning between two modalities of PAN and MS images.

Bijective Mapping Network for Shadow Removal

no code implementations CVPR 2022 Yurui Zhu, Jie Huang, Xueyang Fu, Feng Zhao, Qibin Sun, Zheng-Jun Zha

Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature.

Shadow Removal

Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening

1 code implementation CVPR 2022 Gang Yang, Man Zhou, Keyu Yan, Aiping Liu, Xueyang Fu, Fan Wang

Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.

Denoising

Dreaming To Prune Image Deraining Networks

no code implementations CVPR 2022 Weiqi Zou, Yang Wang, Xueyang Fu, Yang Cao

It is based on our observation that deep degradation representations can be clustered by degradation characteristics (types of rain) while independent of image content.

Model Compression Rain Removal

Unfolding Taylor's Approximations for Image Restoration

no code implementations NeurIPS 2021 Man Zhou, Zeyu Xiao, Xueyang Fu, Aiping Liu, Gang Yang, Zhiwei Xiong

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.

Image Restoration

Image De-Raining via Continual Learning

no code implementations CVPR 2021 Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, Zheng-Jun Zha

The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks.

Computer Vision Continual Learning

Space-Time Distillation for Video Super-Resolution

no code implementations CVPR 2021 Zeyu Xiao, Xueyang Fu, Jie Huang, Zhen Cheng, Zhiwei Xiong

In this paper, we aim to improve the performance of compact VSR networks without changing their original architectures, through a knowledge distillation approach that transfers knowledge from a complicated VSR network to a compact one.

Knowledge Distillation Video Super-Resolution

Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement

1 code implementation1 Feb 2021 Zhenqi Fu, Xueyang Fu, Yue Huang, Xinghao Ding

Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image with its low-quality version.

Image Enhancement

Learning Dual Priors for JPEG Compression Artifacts Removal

no code implementations ICCV 2021 Xueyang Fu, Xi Wang, Aiping Liu, Junwei Han, Zheng-Jun Zha

Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively.

Improving De-Raining Generalization via Neural Reorganization

no code implementations ICCV 2021 Jie Xiao, Man Zhou, Xueyang Fu, Aiping Liu, Zheng-Jun Zha

Equipped with our NR algorithm, the deep model can be trained on a list of synthetic rainy datasets by overcoming catastrophic forgetting, making it a general-version de-raining network.

Knowledge Distillation

Attack-Guided Perceptual Data Generation for Real-World Re-Identification

no code implementations ICCV 2021 Yukun Huang, Xueyang Fu, Zheng-Jun Zha

In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e. g., cross-resolution and insufficient lighting.

Person Re-Identification Representation Learning

Cross-Patch Graph Convolutional Network for Image Denoising

no code implementations ICCV 2021 Yao Li, Xueyang Fu, Zheng-Jun Zha

However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image.

Image Denoising

Real-world Person Re-Identification via Degradation Invariance Learning

no code implementations CVPR 2020 Yukun Huang, Zheng-Jun Zha, Xueyang Fu, Richang Hong, Liang Li

Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e. g., low-resolution, weak illumination, blurring and adverse weather.

Image Restoration Person Re-Identification +1

Noise2Blur: Online Noise Extraction and Denoising

no code implementations3 Dec 2019 Huangxing Lin, Weihong Zeng, Xinghao Ding, Xueyang Fu, Yue Huang, John Paisley

Using the new image pair, the denoising network learns to generate clean and high-quality images from noisy observations.

Image Denoising

JPEG Artifacts Reduction via Deep Convolutional Sparse Coding

no code implementations ICCV 2019 Xueyang Fu, Zheng-Jun Zha, Feng Wu, Xinghao Ding, John Paisley

To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture.

A Variational Pan-Sharpening With Local Gradient Constraints

no code implementations CVPR 2019 Xueyang Fu, Zihuang Lin, Yue Huang, Xinghao Ding

Then a more accurate spatial preservation based on local gradient constraints is incorporated into the objective to fully utilize spatial information contained in the PAN image.

A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal

no code implementations24 Nov 2018 Huangxing Lin, Xueyang Fu, Changxing Jing, Xinghao Ding, Yue Huang

Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens.

Rain Removal

A Deep Tree-Structured Fusion Model for Single Image Deraining

no code implementations21 Nov 2018 Xueyang Fu, Qi Qi, Yue Huang, Xinghao Ding, Feng Wu, John Paisley

We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem.

Single Image Deraining

Lightweight Pyramid Networks for Image Deraining

no code implementations16 May 2018 Xueyang Fu, Borong Liang, Yue Huang, Xinghao Ding, John Paisley

In this paper, we propose a lightweight pyramid of networks (LPNet) for single image deraining.

Single Image Deraining

Residual-Guide Feature Fusion Network for Single Image Deraining

no code implementations20 Apr 2018 Zhiwen Fan, Huafeng Wu, Xueyang Fu, Yue Hunag, Xinghao Ding

Single image rain streaks removal is extremely important since rainy images adversely affect many computer vision systems.

Computer Vision Single Image Deraining

PanNet: A Deep Network Architecture for Pan-Sharpening

no code implementations ICCV 2017 Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, John Paisley

We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation.

Removing Rain From Single Images via a Deep Detail Network

no code implementations CVPR 2017 Xueyang Fu, Jia-Bin Huang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley

We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN).

Denoising Rain Removal

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI

no code implementations12 Feb 2013 Yue Huang, John Paisley, Qin Lin, Xinghao Ding, Xueyang Fu, Xiao-Ping Zhang

The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables.

Denoising Dictionary Learning +2

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