Search Results for author: Dong Liang

Found 38 papers, 15 papers with code

Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI

no code implementations18 Dec 2021 Zhuo-Xu Cui, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Kankan Zhao, Ziwen Ke, Wenqi Huang, Haifeng Wang, Yanjie Zhu, Dong Liang

Specifically, focusing on accelerated MRI, we unroll a zeroth-order algorithm, of which the network module represents the regularizer itself, so that the network output can be still covered by the regularization model.

MRI Reconstruction

Semantically Contrastive Learning for Low-light Image Enhancement

1 code implementation13 Dec 2021 Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou

Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.

Contrastive Learning Low-Light Image Enhancement +1

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

1 code implementation13 Dec 2021 Dong Liang, Qixiang Geng, Zongqi Wei, Dmitry A. Vorontsov, Ekaterina L. Kim, Mingqiang Wei, Huiyu Zhou

On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0. 40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3. 08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone.

Object Detection In Aerial Images Robust Object Detection +1

Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism

no code implementations1 Dec 2021 Minghan Fu, Yanhua Duan, Zhaoping Cheng, Wenjian Qin, Ying Wang, Dong Liang, Zhanli Hu

The derived architecture is referred to as the Teacher-Student Consistency Network (TSC-Net), which consists of the teacher network and the student network with identical architecture.

Contrastive Learning Image Denoising +1

MRI Reconstruction Using Deep Energy-Based Model

1 code implementation7 Sep 2021 Yu Guan, Zongjiang Tu, Shanshan Wang, Qiegen Liu, Yuhao Wang, Dong Liang

In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image.

Image Generation MRI Reconstruction

MPI: Multi-receptive and Parallel Integration for Salient Object Detection

1 code implementation8 Aug 2021 Han Sun, Jun Cen, Ningzhong Liu, Dong Liang, Huiyu Zhou

The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection.

Object Detection Salient Object Detection

A Scalable 256-Elements E-Band Phased-Array Transceiver for Broadband Communication

no code implementations20 Jun 2021 Xu Li, Wenyao Zhai, Morris Repeta, Hua Cai, Tyler Ross, Kimia Ansari, Sam Tiller, Hari Krishna Pothula, Dong Liang, Fan Yang, Yibo Lyu, Songlin Shuai, Guangjian Wang, Wen Tong

For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity.

Active Terahertz Imaging Dataset for Concealed Object Detection

1 code implementation8 May 2021 Dong Liang, Fei Xue, Ling Li

Concealed object detection in Terahertz imaging is an urgent need for public security and counter-terrorism.

Object Detection

Learning Calibrated-Guidance for Object Detection in Aerial Images

1 code implementation21 Mar 2021 Zongqi Wei, Dong Liang, Dong Zhang, Liyan Zhang, Qixiang Geng, Mingqiang Wei, Huiyu Zhou

Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.

Object Detection In Aerial Images

Deep Low-rank plus Sparse Network for Dynamic MR Imaging

1 code implementation26 Oct 2020 Wenqi Huang, Ziwen Ke, Zhuo-Xu Cui, Jing Cheng, Zhilang Qiu, Sen Jia, Leslie Ying, Yanjie Zhu, Dong Liang

However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods.

MRI Reconstruction

Is Each Layer Non-trivial in CNN?

no code implementations9 Sep 2020 Wei Wang, Yanjie Zhu, Zhuoxu Cui, Dong Liang

Convolutional neural network (CNN) models have achieved great success in many fields.

Homotopic Gradients of Generative Density Priors for MR Image Reconstruction

5 code implementations14 Aug 2020 Cong Quan, Jinjie Zhou, Yuanzheng Zhu, Yang Chen, Shan-Shan Wang, Dong Liang, Qiegen Liu

Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.

Denoising MRI Reconstruction

Exploring the parameter reusability of CNN

no code implementations8 Aug 2020 Wei Wang, Lin Cheng, Yanjie Zhu, Dong Liang

In recent times, using small data to train networks has become a hot topic in the field of deep learning.

Semantic Segmentation Transfer Learning

Deep Low-rank Prior in Dynamic MR Imaging

no code implementations22 Jun 2020 Ziwen Ke, Wenqi Huang, Jing Cheng, Zhuoxu Cui, Sen Jia, Haifeng Wang, Xin Liu, Hairong Zheng, Leslie Ying, Yanjie Zhu, Dong Liang

The deep learning methods have achieved attractive performance in dynamic MR cine imaging.

MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images

no code implementations21 May 2020 Yiyang Shen, Yidan Feng, Sen Deng, Dong Liang, Jing Qin, Haoran Xie, Mingqiang Wei

We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.

Rain Removal

Co-occurrence Background Model with Superpixels for Robust Background Initialization

no code implementations29 Mar 2020 Wenjun Zhou, Yuheng Deng, Bo Peng, Dong Liang, Shun'ichi Kaneko

Background initialization is an important step in many high-level applications of video processing, ranging from video surveillance to video inpainting. However, this process is often affected by practical challenges such as illumination changes, background motion, camera jitter and intermittent movement, etc. In this paper, we develop a co-occurrence background model with superpixel segmentation for robust background initialization.


SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning

no code implementations3 Feb 2020 Hongyu Li, Zifei Liang, Chaoyi Zhang, Ruiying Liu, Jing Li, Weihong Zhang, Dong Liang, Bowen Shen, Xiaoliang Zhang, Yulin Ge, Jiangyang Zhang, Leslie Ying

We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability.

An Unsupervised Deep Learning Method for Multi-coil Cine MRI

1 code implementation20 Dec 2019 Ziwen Ke, Jing Cheng, Leslie Ying, Hairong Zheng, Yanjie Zhu, Dong Liang

Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied.

MRI Reconstruction

Learning Priors in High-frequency Domain for Inverse Imaging Reconstruction

1 code implementation23 Oct 2019 Zhuonan He, Jinjie Zhou, Dong Liang, Yuhao Wang, Qiegen Liu

Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging.

Denoising Dictionary Learning

IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI

1 code implementation24 Sep 2019 Yiling Liu, Qiegen Liu, Minghui Zhang, Qingxin Yang, Shan-Shan Wang, Dong Liang

To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.

Compressive Sensing Denoising

Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction

1 code implementation3 Sep 2019 Siyuan Wang, Junjie Lv, Yuanyuan Hu, Dong Liang, Minghui Zhang, Qiegen Liu

At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE).

Compressive Sensing Denoising +1

Model Learning: Primal Dual Networks for Fast MR imaging

no code implementations7 Aug 2019 Jing Cheng, Haifeng Wang, Leslie Ying, Dong Liang

Experi-ments on in vivo MR data demonstrate that the proposed method achieves supe-rior MR reconstructions from highly undersampled k-space data over other state-of-the-art image reconstruction methods.

Image Reconstruction

Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

no code implementations26 Jul 2019 Dong Liang, Jing Cheng, Ziwen Ke, Leslie Ying

Image reconstruction from undersampled k-space data has been playing an important role for fast MRI.

MRI Reconstruction

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

1 code implementation11 Jun 2019 Shan-Shan Wang, Huitao Cheng, Leslie Ying, Taohui Xiao, Ziwen Ke, Xin Liu, Hairong Zheng, Dong Liang

This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network.

Image Reconstruction

CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint

no code implementations18 Jan 2019 Ziwen Ke, Shan-Shan Wang, Huitao Cheng, Leslie Ying, Qiegen Liu, Hairong Zheng, Dong Liang

In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN.

PCGAN: Partition-Controlled Human Image Generation

1 code implementation25 Nov 2018 Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou

Human image generation is a very challenging task since it is affected by many factors.

Image Generation

DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training

no code implementations30 Sep 2018 Shan-Shan Wang, Ziwen Ke, Huitao Cheng, Sen Jia, Ying Leslie, Hairong Zheng, Dong Liang

Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time.

Image Reconstruction

Leveraging Elastic Demand for Forecasting

no code implementations9 Sep 2018 Houtao Deng, Ganesh Krishnan, Ji Chen, Dong Liang

Demand variance can result in a mismatch between planned supply and actual demand.

Tensor RPCA by Bayesian CP Factorization With Complex Noise

no code implementations ICCV 2017 Qiong Luo, Zhi Han, Xi'ai Chen, Yao Wang, Deyu Meng, Dong Liang, Yandong Tang

In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG).

The Apps You Use Bring The Blogs to Follow

no code implementations27 Jun 2016 Shi Yue, Zhong Erheng, Rajan Suju, Dong Liang, Tseng Hao-wei, Li Beitao

Blog recommendation is challenging since most mobile users would suffer from the cold start when there are only a limited number of blogs followed by the user.

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

no code implementations31 Aug 2015 Wang James Z., Zhang Yuanyuan, Dong Liang, Li Lin, Srimani Pradip K, Yu Philip S.

To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: parallel document index creation, ontology-graph based query expansion, and retrieval and re-ranking of documents based on user's search intention. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database.


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