986 citations · 2k across the 50 of their papers we have counts for
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Learning Rate Dropout
Huangxing Lin, Weihong Zeng, Xinghao Ding +3
The performance of a deep neural network is highly dependent on its training, and finding better local optimal solutions is the goal of many optimization algorithms. However, exist…
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 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…
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
Liyan Sun, Jiexiang Wang, Yue Huang +3
The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervise…
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…
Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network
Liyan Sun, Zhiwen Fan, Yue Huang +2
The need for fast acquisition and automatic analysis of MRI data is growing in the age of big data. Although compressed sensing magnetic resonance imaging (CS-MRI) has been studied…
Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object Segmentation
Delu Zeng, Yixuan He, Li Liu +4
Although deep CNNs have brought significant improvement to image saliency detection, most CNN based models are sensitive to distortion such as compression and noise. In this paper,…
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction
Liyan Sun, Zhiwen Fan, Yue Huang +2
In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast. The conventional optimizat…
A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
Zhiwen Fan, Liyan Sun, Xinghao Ding +3
Compressed sensing MRI is a classic inverse problem in the field of computational imaging, accelerating the MR imaging by measuring less k-space data. The deep neural network model…
A Divide-and-Conquer Approach to Compressed Sensing MRI
Liyan Sun, Zhiwen Fan, Xinghao Ding +3
Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In…
A Deep Error Correction Network for Compressed Sensing MRI
Liyan Sun, Zhiwen Fan, Yue Huang +2
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. The goal is to minimize…
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…