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LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Yi Zhang, Hu Chen, Wenjun Xia +5
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis a…
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Xiang Chen, Wenjun Xia, Yan Liu +3
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs). However, due to the limited photons and countin…
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Wenjun Xia, Zexin Lu, Yongqiang Huang +4
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training co…
Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
Zhuonan He, Yikun Zhang, Yu Guan +4
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compen…
MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction
Wenjun Xia, Zexin Lu, Yongqiang Huang +6
Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstru…
Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images
Yongqiang Huang, Wenjun Xia, Zexin Lu +5
Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging…
Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection
Zerui Shao, Yifei Pu, Jiliu Zhou +2
Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Compon…