Baoshun Shi

Orcid: 0000-0003-4643-3816

According to our database1, Baoshun Shi authored at least 25 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography.
Mach. Learn. Sci. Technol., March, 2024

Provable deep video denoiser using spatial-temporal information for video snapshot compressive imaging: Algorithm and convergence analysis.
Signal Process., January, 2024

Cartoon-texture guided network for low-light image enhancement.
Digit. Signal Process., January, 2024

Coupling Model- and Data-Driven Networks for CT Metal Artifact Reduction.
IEEE Trans. Computational Imaging, 2024

2023
Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging with Convergence Analysis.
IEEE CAA J. Autom. Sinica, November, 2023

Bayesian self-supervised learning allying with Transformer powered compressed sensing imaging.
Digit. Signal Process., August, 2023

DeepCDL-PR: Deep unfolded convolutional dictionary learning with weighted <i>ℓ</i><sub>1</sub>-norm for phase retrieval.
Digit. Signal Process., May, 2023

Provable General Bounded Denoisers for Snapshot Compressive Imaging With Convergence Guarantee.
IEEE Trans. Computational Imaging, 2023

LG-Net: Local and global complementary priors induced multi-stage progressive network for compressed sensing.
Signal Process., 2023

2022
DualPRNet: Deep Shrinkage Dual Frame Network for Deep Unrolled Phase Retrieval.
IEEE Signal Process. Lett., 2022

Supervised dual tight frame learning with deep thresholding network for phase retrieval.
IET Image Process., 2022

TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction.
CoRR, 2022

Convolutional Sparse Coding with Weighted L1 Norm for Phase Retrieval: Algorithm and Its Deep Unfolded Network.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

A Trainable Bounded Denoiser Using Double Tight Frame Network for Snapshot Compressive Imaging.
Proceedings of the IEEE International Conference on Acoustics, 2022

2020
Deep prior-based sparse representation model for diffraction imaging: A plug-and-play method.
Signal Process., 2020

Compressed sensing MRI based on the hybrid regularization by denoising and the epigraph projection.
Signal Process., 2020

2019
PPR: Plug-and-play regularization model for solving nonlinear imaging inverse problems.
Signal Process., 2019

Multi-scale Cross-path Concatenation Residual Network for Poisson denoising.
IET Image Process., 2019

2018
Coded diffraction imaging via double sparse regularization model.
Digit. Signal Process., 2018

FASPR: A fast sparse phase retrieval algorithm via the epigraph concept.
Digit. Signal Process., 2018

SBM3D: Sparse Regularization Model Induced by BM3D for Weighted Diffraction Imaging.
IEEE Access, 2018

2017
Transfer orthogonal sparsifying transform learning for phase retrieval.
Digit. Signal Process., 2017

Compressed Sensing MRI With Phase Noise Disturbance Based on Adaptive Tight Frame and Total Variation.
IEEE Access, 2017

2016
Compressed sensing magnetic resonance imaging based on dictionary updating and block-matching and three-dimensional filtering regularisation.
IET Image Process., 2016

2015
Sparse representation utilizing tight frame for phase retrieval.
EURASIP J. Adv. Signal Process., 2015


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