986 citations · 1.2k across the 13 of their papers we have counts for
13 papers
Decoupling Degradation and Content Processing for Adverse Weather Image Restoration
Xi Wang, Xueyang Fu, Peng-Tao Jiang +4
Adverse weather image restoration strives to recover clear images from those affected by various weather types, such as rain, haze, and snow. Each weather type calls for a tailored…
Revisiting Single Image Reflection Removal In the Wild
Yurui Zhu, Xueyang Fu, Peng-Tao Jiang +5
This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions, examining it from two angles: the collection pipeline of real reflection pair…
Unfolding Taylor's Approximations for Image Restoration
Man Zhou, Zeyu Xiao, Xueyang Fu +3
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…
Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement
Zhenqi Fu, Xueyang Fu, Yue Huang +1
To improve the quality of underwater images, various kinds of underwater image enhancement (UIE) operators have been proposed during the past few years. However, the lack of effect…
Real-world Person Re-Identification via Degradation Invariance Learning
Yukun Huang, Zheng-Jun Zha, Xueyang Fu +2
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. On…
Noise2Blur: Online Noise Extraction and Denoising
Huangxing Lin, Weihong Zeng, Xinghao Ding +3
We propose a new framework called Noise2Blur (N2B) for training robust image denoising models without pre-collected paired noisy/clean images. The training of the model requires on…
Rain O'er Me: Synthesizing real rain to derain with data distillation
Huangxing Lin, Yanlong Li, Xinghao Ding +3
We present a supervised technique for learning to remove rain from images without using synthetic rain software. The method is based on a two-stage data distillation approach: 1) A…
A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal
Huangxing Lin, Xueyang Fu, Changxing Jing +2
Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens. In this paper, we propose a new Adjacent Aggregation Network (A^2N…
A Deep Tree-Structured Fusion Model for Single Image Deraining
Xueyang Fu, Qi Qi, Yue Huang +3
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem. We argue that by effectively aggregating features, a rel…
Lightweight Pyramid Networks for Image Deraining
Xueyang Fu, Borong Liang, Yue Huang +2
Existing deep convolutional neural networks have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential applica…
Residual-Guide Feature Fusion Network for Single Image Deraining
Zhiwen Fan, Huafeng Wu, Xueyang Fu +2
Single image rain streaks removal is extremely important since rainy images adversely affect many computer vision systems. Deep learning based methods have found great success in i…
Clearing the Skies: A deep network architecture for single-image rain removal
Xueyang Fu, Jiabin Huang, Xinghao Ding +2
We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mappi…
Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI
Yue Huang, John Paisley, Qin Lin +3
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the im…