Search Results for author:
Found 63 papers, 23 papers with code
Nonlinear Bipartite Output Regulation with Application to Turing Pattern
In this paper, a bipartite output regulation problem is solved for a class of nonlinear multi-agent systems subject to static signed communication networks.
Model-driven CT reconstruction algorithm for nano-resolution X-ray phase contrast imaging
In conclusion, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer based hard X-ray nPCT imaging system.
Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model
JPD of MRI and noise-added PET was learned in the diffusion process.
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI
We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable.
ALL-E: Aesthetics-guided Low-light Image Enhancement
In this paper, we propose a new paradigm, i. e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward.
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
Diffusion models are a leading method for image generation and have been successfully applied in magnetic resonance imaging (MRI) reconstruction.
Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval
Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e. g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset.
Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements.
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction.
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection
However, existing active learning methods are mainly with class-balanced setting and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenario due to the long-tailed class distribution and dense small objects in aerial scenes.
Deep unfolding as iterative regularization for imaging inverse problems
Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems.
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation
In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.
Active CT Reconstruction with a Learned Sampling Policy
Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.
LBF:Learnable Bilateral Filter For Point Cloud Denoising
Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising.
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction
Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction.
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.
K-UNN: k-Space Interpolation With Untrained Neural Network
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data.
High-Frequency Space Diffusion Models for Accelerated MRI
Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation.
Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)
Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR T1\r{ho} imaging.
Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation
The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.
PS-Net: Learned Partially Separable Model for Dynamic MR Imaging
In this paper, we propose a learned low-rank method for dynamic MR imaging.
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI).
Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology.
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.
Variable Augmented Network for Invertible MR Coil Compression
Additionally, its performance is not susceptible to different number of virtual coils.
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI
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.
Semantically Contrastive Learning for Low-light Image Enhancement
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
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.
Ranked #12 on
Object Detection In Aerial Images
on DOTA
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism
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.
Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning
Methods: The proposed framework consists of a reconstruction module and a generative module.
MRI Reconstruction Using Deep Energy-Based Model
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.
MPI: Multi-receptive and Parallel Integration for Salient Object Detection
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.
A Scalable 256-Elements E-Band Phased-Array Transceiver for Broadband Communication
For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity.
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation
In this paper, we propose a novel DG scheme of episodic training with task augmentation on medical imaging classification.
Active Terahertz Imaging Dataset for Concealed Object Detection
Concealed object detection in Terahertz imaging is an urgent need for public security and counter-terrorism.
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem.
Learning Calibrated-Guidance for Object Detection in Aerial Images
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.
Deep Manifold Learning for Dynamic MR Imaging
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
Deep Low-rank plus Sparse Network for Dynamic MR Imaging
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.
Is Each Layer Non-trivial in CNN?
Convolutional neural network (CNN) models have achieved great success in many fields.
Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.
Exploring the parameter reusability of CNN
In recent times, using small data to train networks has become a hot topic in the field of deep learning.
Deep Low-rank Prior in Dynamic MR Imaging
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
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.
Co-occurrence Background Model with Superpixels for Robust Background Initialization
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
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
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.
Learning Priors in High-frequency Domain for Inverse Imaging Reconstruction
Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging.
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
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.
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction
At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE).
Model Learning: Primal Dual Networks for Fast MR imaging
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.
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI.
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Usually, acquiring less data is a direct but important strategy to address these issues.
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network.
CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
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
Human image generation is a very challenging task since it is affected by many factors.
Convolutional Interaction Network for Natural Language Inference
Attention-based neural models have achieved great success in natural language inference (NLI).
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time.
Leveraging Elastic Demand for Forecasting
Demand variance can result in a mismatch between planned supply and actual demand.
Tensor RPCA by Bayesian CP Factorization With Complex Noise
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
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
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.
Improvements and Experiments of a Compact Statistical Background Model
Change detection plays an important role in most video-based applications.
