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08/20/2023
Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction
To obtain high-quality positron emission tomography (PET) scans while re...
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11/24/2021
LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes
Pansharpening in remote sensing image aims at acquiring a high-resolutio...
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10/29/2021
Unsupervised PET Reconstruction from a Bayesian Perspective
Positron emission tomography (PET) reconstruction has become an ill-pose...
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05/14/2021
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmen...
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04/03/2021
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Due to the presence of metallic implants, the imaging quality of compute...
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03/24/2021
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Lowering the radiation dose in computed tomography (CT) can greatly redu...
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03/18/2021
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification
Hyperspectral Image(HSI) classification is the most vibrant field of res...
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02/16/2021
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Metal implants can heavily attenuate X-rays in computed tomography (CT) ...
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12/13/2020
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Compressed sensing (CS) computed tomography has been proven to be import...
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10/27/2020
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Spectral computed tomography (CT) can reconstruct spectral images from d...
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10/27/2020
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Current mainstream of CT reconstruction methods based on deep learning u...
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06/14/2020
Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection
Moving object detection is critical for automated video analysis in many...
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03/20/2019
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Over the past few years, dictionary learning (DL)-based methods have bee...
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10/15/2018
Sparse-View CT Reconstruction via Convolutional Sparse Coding
Traditional dictionary learning based CT reconstruction methods are patc...
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08/12/2018
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network
Structure-preserved denoising of 3-D magnetic resonance images (MRI) is ...
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07/30/2017
Learned Experts' Assessment-based Reconstruction Network ("LEARN") for Sparse-data CT
Compressive sensing (CS) has proved effective for tomographic reconstruc...
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02/01/2017
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
Given the potential X-ray radiation risk to the patient, low-dose CT has...
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10/02/2016
