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Yan Liu
Researcher at Sichuan University
Publications - 34
Citations - 386
Yan Liu is an academic researcher from Sichuan University. The author has contributed to research in topics: Iterative reconstruction & Computer science. The author has an hindex of 5, co-authored 32 publications receiving 127 citations.
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A new CT metal artifacts reduction algorithm based on fractional-order sinogram inpainting
TL;DR: Simulations show that the proposed new metal artifacts reduction algorithm based on fractional- order total-variation sinogram inpainting model for X-ray computed tomography (CT) is superior to conditional interpolation methods and the classic integral-order total variation model.
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Efficient CT metal artifact reduction based on fractional-order curvature diffusion.
TL;DR: This work proposes a novel metal artifact reduction method based on a fractional-order curvature driven diffusion model for X-ray computed tomography that achieves better performance than existing projection interpolation methods, including linear interpolation and total variation.
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MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction.
Wenjun Xia,Zexin Lu,Yongqiang Huang,Zuoqiang Shi,Yan Liu,Hu Chen,Yang Chen,Jiliu Zhou,Yi Zhang +8 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel low-dose computed tomography (LDCT) reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces.
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Multi-task short-term reactive and active load forecasting method based on attention-LSTM model
Bendera Kematian,Jiaqi Qin,Yi Zhang,Shixiong Fan,Xiaonan Hu,Yongqiang Huang,Zexin Lu,Yan Liu +7 more
TL;DR: This paper proposes a novel multi-task load forecasting method for predicting both active and reactive power simultaneously, using the long short-term memory (LSTM) architecture in the backbone prediction model, supported by an attention mechanism to prevent performance deterioration.
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CT Reconstruction With PDF: Parameter-Dependent Framework for Data From Multiple Geometries and Dose Levels
TL;DR: In this paper, the geometry and dose level are parameterized and fed into two multilayer perceptrons (MLP) to modulate the feature maps of the CT reconstruction network, which condition the network outputs on different geometries and dose levels.