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2020 – today
- 2025
[j27]Baoshun Shi
, Yating Gao, Runze Zhang:
UPrime: Unrolled Phase Retrieval Iterative Method with provable convergence. Signal Process. 226: 109640 (2025)- 2024
- [j26]Jiale Chen, Qiusheng Lian, Baoshun Shi, Chengli Gao:
A Two-Phase Reference-Free Approach for Low-Light Image Enhancement. Circuits Syst. Signal Process. 43(6): 3553-3575 (2024) - [j25]Lingyan Li, Chunzi Zhu, Jiale Chen, Baoshun Shi, Qiusheng Lian:
Self-Supervised Normalizing Flow for Jointing Low-Light Enhancement and Deblurring. Circuits Syst. Signal Process. 43(9): 5727-5748 (2024) - [j24]Baoshun Shi, Chunzi Zhu, Lingyan Li, Huagui Huang:
Cartoon-texture guided network for low-light image enhancement. Digit. Signal Process. 144: 104271 (2024) - [j23]Baoshun Shi, Ke Jiang, Shaolei Zhang, Qiusheng Lian, Yanwei Qin, Yunsong Zhao:
Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography. Mach. Learn. Sci. Technol. 5(1): 15010 (2024) - [j22]Baoshun Shi, Dan Li, Yuxin Wang, Yueming Su, Qiusheng Lian:
Provable deep video denoiser using spatial-temporal information for video snapshot compressive imaging: Algorithm and convergence analysis. Signal Process. 214: 109236 (2024) - [j21]Yueming Su, Qiusheng Lian, Dan Zhang, Baoshun Shi:
Transformer based Douglas-Rachford unrolling network for compressed sensing. Signal Process. Image Commun. 127: 117153 (2024) - [j20]Baoshun Shi, Shaolei Zhang, Fu ZhaoRan:
Artifact Region-Aware Transformer: Global Context Helps CT Metal Artifact Reduction. IEEE Signal Process. Lett. 31: 1249-1253 (2024) - [j19]Baoshun Shi, Shaolei Zhang, Ke Jiang, Qiusheng Lian:
Coupling Model- and Data-Driven Networks for CT Metal Artifact Reduction. IEEE Trans. Computational Imaging 10: 415-428 (2024) - 2023
- [j18]Baoshun Shi, Yating Gao, Yueming Su, Qiusheng Lian:
DeepCDL-PR: Deep unfolded convolutional dictionary learning with weighted ℓ1-norm for phase retrieval. Digit. Signal Process. 136: 103971 (2023) - [j17]Yueming Su, Yongli Yang, Baoshun Shi, Yuwei Zhang:
Bayesian self-supervised learning allying with Transformer powered compressed sensing imaging. Digit. Signal Process. 140: 104120 (2023) - [j16]Baoshun Shi, Kexun Liu:
Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging with Convergence Analysis. IEEE CAA J. Autom. Sinica 10(11): 2136-2153 (2023) - [j15]Qiusheng Lian, Yueming Su, Baoshun Shi, Dan Zhang:
LG-Net: Local and global complementary priors induced multi-stage progressive network for compressed sensing. Signal Process. 202: 108737 (2023) - [j14]Baoshun Shi, Yuxin Wang, Dan Li:
Provable General Bounded Denoisers for Snapshot Compressive Imaging With Convergence Guarantee. IEEE Trans. Computational Imaging 9: 55-69 (2023) - 2022
- [j13]Baoshun Shi, Qiusheng Lian, Yueming Su:
Supervised dual tight frame learning with deep thresholding network for phase retrieval. IET Image Process. 16(10): 2752-2758 (2022) - [j12]Baoshun Shi, Qiusheng Lian:
DualPRNet: Deep Shrinkage Dual Frame Network for Deep Unrolled Phase Retrieval. IEEE Signal Process. Lett. 29: 1177-1181 (2022) - [c2]Baoshun Shi, Yuxin Wang, Qiusheng Lian:
A Trainable Bounded Denoiser Using Double Tight Frame Network for Snapshot Compressive Imaging. ICASSP 2022: 1516-1520 - [c1]Baoshun Shi, Yating Gao, Ke Jiang, Qiusheng Lian:
Convolutional Sparse Coding with Weighted L1 Norm for Phase Retrieval: Algorithm and Its Deep Unfolded Network. ICIP 2022: 1746-1750 - [i1]Baoshun Shi, Ke Jiang, Shaolei Zhang, Qiusheng Lian, Yanwei Qin:
TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction. CoRR abs/2211.07190 (2022) - 2020
- [j11]Baoshun Shi, Qiusheng Lian, Huibin Chang:
Deep prior-based sparse representation model for diffraction imaging: A plug-and-play method. Signal Process. 168 (2020) - [j10]Qiusheng Lian, Fan Xiaoyu, Baoshun Shi, Xiaohua Zhang:
Compressed sensing MRI based on the hybrid regularization by denoising and the epigraph projection. Signal Process. 170: 107444 (2020)
2010 – 2019
- 2019
- [j9]Yueming Su, Qiusheng Lian, Xiaohua Zhang, Baoshun Shi, Fan Xiaoyu:
Multi-scale Cross-path Concatenation Residual Network for Poisson denoising. IET Image Process. 13(8): 1295-1303 (2019) - [j8]Baoshun Shi, Qiusheng Lian, Fan Xiaoyu:
PPR: Plug-and-play regularization model for solving nonlinear imaging inverse problems. Signal Process. 162: 83-96 (2019) - 2018
- [j7]Baoshun Shi, Qiusheng Lian, Shuzhen Chen, Fan Xiaoyu:
SBM3D: Sparse Regularization Model Induced by BM3D for Weighted Diffraction Imaging. IEEE Access 6: 46266-46280 (2018) - [j6]Baoshun Shi, Qiusheng Lian, Shuzhen Chen, Ye Tian, Fan Xiaoyu:
Coded diffraction imaging via double sparse regularization model. Digit. Signal Process. 79: 23-33 (2018) - [j5]Baoshun Shi, Shuzhen Chen, Ye Tian, Fan Xiaoyu, Qiusheng Lian:
FASPR: A fast sparse phase retrieval algorithm via the epigraph concept. Digit. Signal Process. 80: 12-26 (2018) - 2017
- [j4]Fan Xiaoyu, Qiusheng Lian, Baoshun Shi:
Compressed Sensing MRI With Phase Noise Disturbance Based on Adaptive Tight Frame and Total Variation. IEEE Access 5: 19311-19321 (2017) - [j3]Qiusheng Lian, Baoshun Shi, Shuzhen Chen:
Transfer orthogonal sparsifying transform learning for phase retrieval. Digit. Signal Process. 62: 11-25 (2017) - 2016
- [j2]Baoshun Shi, Qiusheng Lian, Shuzhen Chen:
Compressed sensing magnetic resonance imaging based on dictionary updating and block-matching and three-dimensional filtering regularisation. IET Image Process. 10(1): 68-79 (2016) - 2015
- [j1]Baoshun Shi, Qiusheng Lian, Shuzhen Chen:
Sparse representation utilizing tight frame for phase retrieval. EURASIP J. Adv. Signal Process. 2015: 96 (2015)
Coauthor Index
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