J
Jiliu Zhou
Researcher at Chengdu University of Information Technology
Publications - 236
Citations - 6479
Jiliu Zhou is an academic researcher from Chengdu University of Information Technology. The author has contributed to research in topics: Computer science & Iterative reconstruction. The author has an hindex of 30, co-authored 197 publications receiving 4223 citations. Previous affiliations of Jiliu Zhou include Sichuan University.
Papers
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Journal ArticleDOI
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
TL;DR: This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.
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Low-dose CT via convolutional neural network
TL;DR: A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion, demonstrating a great potential of the proposed method on artifact reduction and structure preservation.
Journal ArticleDOI
Fractional Differential Mask: A Fractional Differential-Based Approach for Multiscale Texture Enhancement
Yi-Fei Pu,Jiliu Zhou,Xiao Yuan +2 more
TL;DR: Experiments show that, for rich-grained digital image, the capability of nonlinearly enhancing complex texture details in smooth area by fractional differential-based approach appears obvious better than by traditional integral-based algorithms.
Journal ArticleDOI
LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT
Hu Chen,Yi Zhang,Yunjin Chen,Junfeng Zhang,Weihua Zhang,Huaiqiang Sun,Yang Lv,Peixi Liao,Jiliu Zhou,Ge Wang +9 more
TL;DR: In this paper, a learned experts' assessment-based reconstruction network (LEARN) was proposed for sparse-data computed tomography (CT) reconstruction, which utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms.
Journal ArticleDOI
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
TL;DR: Zhang et al. as discussed by the authors combined the autoencoder, the deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging.