54 citations · 523 across the 37 of their papers we have counts for
28 papers · 1 filter
Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
Liang Chen, Jiawei Zhang, Zhenhua Li +4
Images taken under the low-light condition often contain blur and saturated pixels at the same time. Deblurring images with saturated pixels is quite challenging. Because of the li…
Deep Dynamic Scene Deblurring from Optical Flow
Jiawei Zhang, Jinshan Pan, Daoye Wang +5
Deblurring can not only provide visually more pleasant pictures and make photography more convenient, but also can improve the performance of objection detection as well as trackin…
Self-Supervised Intensity-Event Stereo Matching
Jinjin Gu, Jinan Zhou, Ringo Sai Wo Chu +5
Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption. Despite t…
NTIRE 2022 Challenge on Perceptual Image Quality Assessment
Jinjin Gu, Haoming Cai, Chao Dong +2
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (…
EfficientFCN: Holistically-guided Decoding for Semantic Segmentation
Jianbo Liu, Junjun He, Jiawei Zhang +2
Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (…
Learning a Reinforced Agent for Flexible Exposure Bracketing Selection
Zhouxia Wang, Jiawei Zhang, Mude Lin +3
Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion. Unlike previous methods t…
Learning Event-Based Motion Deblurring
Zhe Jiang, Yu Zhang, Dongqing Zou +3
Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the significant loss of motion information in the blurring process. For event-based cameras,…
Learning to Predict Context-adaptive Convolution for Semantic Segmentation
Jianbo Liu, Junjun He, Jimmy S. Ren +2
Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for…
Generalizing Monocular 3D Human Pose Estimation in the Wild
Luyang Wang, Yan Chen, Zhenhua Guo +4
The availability of the large-scale labeled 3D poses in the Human3.6M dataset plays an important role in advancing the algorithms for 3D human pose estimation from a still image. W…
DAVANet: Stereo Deblurring with View Aggregation
Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo +3
Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles. However, they also suffer from blurry images in dy…
Spatio-Temporal Filter Adaptive Network for Video Deblurring
Shangchen Zhou, Jiawei Zhang, Jinshan Pan +3
Video deblurring is a challenging task due to the spatially variant blur caused by camera shake, object motions, and depth variations, etc. Existing methods usually estimate optica…
Very Power Efficient Neural Time-of-Flight
Yan Chen, Jimmy Ren, Xuanye Cheng +2
Time-of-Flight (ToF) cameras require active illumination to obtain depth information thus the power of illumination directly affects the performance of ToF cameras. Traditional ToF…
DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras
Ruichao Xiao, Wenxiu Sun, Jiahao Pang +2
With the developments of dual-lens camera modules,depth information representing the third dimension of thecaptured scenes becomes available for smartphones. It isestimated by ster…
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
Xiaoyang Guo, Hongsheng Li, Shuai Yi +2
Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving. Existing supervis…
Physics-Based Generative Adversarial Models for Image Restoration and Beyond
Jinshan Pan, Jiangxin Dong, Yang Liu +5
We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed,…
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
Zhouxia Wang, Tianshui Chen, Jimmy Ren +3
Social relationships (e.g., friends, couple etc.) form the basis of the social network in our daily life. Automatically interpreting such relationships bears a great potential for…
Learning Selfie-Friendly Abstraction from Artistic Style Images
Yicun Liu, Jimmy Ren, Jianbo Liu +2
Artistic style transfer can be thought as a process to generate different versions of abstraction of the original image. However, most of the artistic style transfer operators are…
Learning Dual Convolutional Neural Networks for Low-Level Vision
Jinshan Pan, Sifei Liu, Deqing Sun +8
In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and deha…
3D Human Pose Estimation in the Wild by Adversarial Learning
Wei Yang, Wanli Ouyang, Xiaolong Wang +3
Recently, remarkable advances have been achieved in 3D human pose estimation from monocular images because of the powerful Deep Convolutional Neural Networks (DCNNs). Despite their…
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
Jiahao Pang, Wenxiu Sun, Chengxi Yang +4
Despite the recent success of stereo matching with convolutional neural networks (CNNs), it remains arduous to generalize a pre-trained deep stereo model to a novel domain. A major…
Single View Stereo Matching
Yue Luo, Jimmy Ren, Mude Lin +4
Previous monocular depth estimation methods take a single view and directly regress the expected results. Though recent advances are made by applying geometrically inspired loss fu…
LSTM Pose Machines
Yue Luo, Jimmy Ren, Zhouxia Wang +5
We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior…
Image Dehazing using Bilinear Composition Loss Function
Hui Yang, Jinshan Pan, Qiong Yan +3
In this paper, we introduce a bilinear composition loss function to address the problem of image dehazing. Previous methods in image dehazing use a two-stage approach which first e…
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
Jiahao Pang, Wenxiu Sun, Jimmy SJ. Ren +2
Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performanc…