167 citations · 424 across the 41 of their papers we have counts for
42 papers
A Thresholding Based Operator-Splitting Method for Curvature-Regularized Surface Reconstruction
Yang Hu, Hao Liu, Dong Wang +1
Surface reconstruction from point clouds is a fundamental problem in computational geometry with broad applications in computer graphics, medical imaging, and manufacturing. In thi…
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation
Juntong Fan, Debesh Jha, Tieyong Zeng +1
Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues. This similarity gives ri…
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
Jing-Xiao Liao, Sheng-Lai Wei, Chen-Long Xie +5
Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be…
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance
Yiming Cui, Lecheng Ruan, Hang-Cheng Dong +4
The networks for point cloud tasks are expected to be invariant when the point clouds are affinely transformed such as rotation and reflection. So far, relative to the rotational i…
Deep ReLU Networks Have Surprisingly Simple Polytopes
Feng-Lei Fan, Wei Huang, Xiangru Zhong +4
A ReLU network is a piecewise linear function over polytopes. Figuring out the properties of such polytopes is of fundamental importance for the research and development of neural…
Recognizable Information Bottleneck
Yilin Lyu, Xin Liu, Mingyang Song +4
Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalizat…
Fast MRI Reconstruction via Edge Attention
Hanhui Yang, Juncheng Li, Lok Ming Lui +3
Fast and accurate MRI reconstruction is a key concern in modern clinical practice. Recently, numerous Deep-Learning methods have been proposed for MRI reconstruction, however, they…
EWT: Efficient Wavelet-Transformer for Single Image Denoising
Juncheng Li, Bodong Cheng, Ying Chen +2
Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatl…
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees
Ziwen Wang, Yancheng Yuan, Jiaming Ma +2
In this paper, we propose a randomly projected convex clustering model for clustering a collection of high dimensional data points in with hidden clusters. C…
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
Hansheng Guo, Juncheng Li, Guangwei Gao +2
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous m…
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
Feng-Lei Fan, Hang-Cheng Dong, Zhongming Wu +4
Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into art…
Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm
Dong Li, Shuisheng Zhou, Tieyong Zeng +1
K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters k has to be given…
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
Zhifan Gao, Yifeng Guo, Jiajing Zhang +2
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techni…
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
Ao Chen, Jinghua Zhang, Md Mamunur Rahaman +6
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and reso…
Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks
Feng-Lei Fan, Yingxin Li, Hanchuan Peng +2
Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened under…
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework
Tingting Wu, Wenna Wu, Ying Yang +2
The Retinex model is one of the most representative and effective methods for low-light image enhancement. However, the Retinex model does not explicitly tackle the noise problem,…
Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior
Xiaole Tang, Xile Zhao, Jun Liu +3
Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-…
Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks
Jianfei Li, Chaoyan Huang, Raymond Chan +3
Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging. It i…
Snow Mask Guided Adaptive Residual Network for Image Snow Removal
Bodong Cheng, Juncheng Li, Ying Chen +2
Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely commo…
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification
Cheng Chang, Tieyong Zeng
Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a data…
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Juncheng Li, Hanhui Yang, Qiaosi Yi +4
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of paramete…
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
Guangwei Gao, Zhengxue Wang, Juncheng Li +3
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world…
Convex Augmentation for Total Variation Based Phase Retrieval
Jianwei Niu, Hok Shing Wong, Tieyong Zeng
Phase retrieval is an important problem with significant physical and industrial applications. In this paper, we consider the case where the magnitude of the measurement of an unde…
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Guangwei Gao, Zixiang Xu, Juncheng Li +3
Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by jointly training with f…