Search Results for author:
Found 9 papers, 5 papers with code
Low-resolution Prior Equilibrium Network for CT Reconstruction
The unrolling method has been investigated for learning variational models in X-ray computed tomography.
ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation
The convolutional neural network-based methods have become more and more popular for medical image segmentation due to their outstanding performance.
Ranked #8 on
Medical Image Segmentation
on Synapse multi-organ CT
(using extra training data)
A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images
Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients.
MetaUE: Model-based Meta-learning for Underwater Image Enhancement
The meta-learning strategy is used to obtain a pre-trained model on the synthetic underwater dataset, which contains different types of degradation to cover the various underwater environments.
Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data
Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections.
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser
Due to the development of deep learning-based denoisers, the plug-and-play strategy has achieved great success in image restoration problems.
LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction
The noisy incomplete projection data results in the ill-posedness of the inverse problems.
Fast Multi-grid Methods for Minimizing Curvature Energy
In this paper, we propose fast multi-grid algorithms for minimizing both mean curvature and Gaussian curvature energy functionals without sacrificing accuracy for efficiency.
Bilinear Constraint based ADMM for Mixed Poisson-Gaussian Noise Removal
In this paper, we propose new operator-splitting algorithms for the total variation regularized infimal convolution (TV-IC) model [4] in order to remove mixed Poisson-Gaussian(MPG) noise.
