150 citations · 583 across the 49 of their papers we have counts for
9 papers · 1 filter
Self-Verification in Image Denoising
Huangxing Lin, Yihong Zhuang, Delu Zeng +3
We devise a new regularization, called self-verification, for image denoising. This regularization is formulated using a deep image prior learned by the network, rather than a trad…
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation
Chenxin Li, Qi Qi, Xinghao Ding +3
Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about the generalization capacity of…
Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation
Chenxin Li, Wenao Ma, Liyan Sun +4
The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a easy task since the fine-detailed connected regions of organ vessel b…
Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label
Wu Wang, Yue Huang, Xinhao Ding
Hyperspectral image fusion (HIF) is critical to a wide range of applications in remote sensing and many computer vision applications. Most traditional HIF methods assume that the o…
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors
Chaoqi Chen, Zebiao Zheng, Yue Huang +2
Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the regi…
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation
Chenxin Li, Yunlong Zhang, Jiongcheng Li +2
The goal of unsupervised anomaly segmentation (UAS) is to detect the pixel-level anomalies unseen during training. It is a promising field in the medical imaging community, e.g, we…
Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification
Chenxin Li, Yunlong Zhang, Zhehan Liang +3
Retinal artery/vein (A/V) classification is a critical technique for diagnosing diabetes and cardiovascular diseases. Although deep learning based methods achieve impressive result…
Underwater Image Enhancement via Learning Water Type Desensitized Representations
Zhenqi Fu, Xiaopeng Lin, Wu Wang +2
We present a novel underwater image enhancement method termed SCNet to improve the image quality meanwhile cope with the degradation diversity caused by the water. SCNet is based o…
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…