Recently, data-driven techniques have demonstrated remarkable effectiven...
Most existing MRI reconstruction methods perform tar-geted reconstructio...
When taking images against strong light sources, the resulting images of...
In the field of parallel imaging (PI), alongside image-domain regulariza...
In this paper, a dynamic dual-graph fusion convolutional network is prop...
This paper explores the application of automated machine learning (AutoM...
This paper presents a mini immersed finite element (IFE) method for solv...
Bias field, which is caused by imperfect MR devices or imaged objects,
i...
Motion artifact is a major challenge in magnetic resonance imaging (MRI)...
Metal artifacts is a major challenge in computed tomography (CT) imaging...
Structural magnetic resonance imaging (sMRI) has shown great clinical va...
In this paper, a bipartite output regulation problem is solved for a cla...
MRI and PET are crucial diagnostic tools for brain diseases, as they pro...
Magnetic resonance imaging (MRI) is known to have reduced signal-to-nois...
Evaluating the performance of low-light image enhancement (LLE) is highl...
Diffusion models are a leading method for image generation and have been...
Searching by image is popular yet still challenging due to the extensive...
Dynamic magnetic resonance image reconstruction from incomplete k-space ...
Recent aerial object detection models rely on a large amount of labeled
...
Recently, deep unfolding methods that guide the design of deep neural
ne...
Achieving accurate and automated tumor segmentation plays an important r...
Computed tomography (CT) is a widely-used imaging technology that assist...
Bilateral filter (BF) is a fast, lightweight and effective tool for imag...
Recently, score-based diffusion models have shown satisfactory performan...
Magnetic resonance imaging serves as an essential tool for clinical
diag...
Recently, untrained neural networks (UNNs) have shown satisfactory
perfo...
Denoising diffusion probabilistic models (DDPMs) have been shown to have...
The morphological changes in knee cartilage (especially femoral and tibi...
Deep learning methods driven by the low-rank regularization have achieve...
Parallel Imaging (PI) is one of the most im-portant and successful
devel...
Parallel imaging is widely used in magnetic resonance imaging as an
acce...
Decreasing magnetic resonance (MR) image acquisition times can potential...
A large number of coils are able to provide enhanced signal-to-noise rat...
Recently, model-driven deep learning unrolls a certain iterative algorit...
Object detection has made tremendous strides in computer vision. Small o...
Low-light image enhancement (LLE) remains challenging due to the unfavor...
Purpose: To propose a novel deep learning-based method called RG-Net
(re...
Purpose: Although recent deep energy-based generative models (EBMs) have...
The semantic representation of deep features is essential for image cont...
Medical imaging datasets usually exhibit domain shift due to the variati...
Concealed object detection in Terahertz imaging is an urgent need for pu...
Improving the image resolution and acquisition speed of magnetic resonan...
Recently, the study on object detection in aerial images has made tremen...
In dynamic MR imaging, L+S decomposition, or robust PCA equivalently, ha...
Deep learning, particularly the generative model, has demonstrated treme...
The deep learning methods have achieved attractive results in dynamic MR...
Rain severely hampers the visibility of scene objects when images are
ca...
Background initialization is an important step in many high-level
applic...
Diffusion tensor imaging (DTI) is widely used to examine the human brain...
Deep learning has achieved good success in cardiac magnetic resonance im...