Sparse representation utilizing tight frame for phase retrieval. - Baoshun Shi; Qiusheng Lian; Shuzhen Chen 002 (EURASIP J. Adv. Signal Process., 2015)

Compressed sensing magnetic resonance imaging based on dictionary updating and block-matching and three-dimensional filtering regularisation. - Baoshun Shi; Qiusheng Lian; Shuzhen Chen 002 (IET Image Process., 2016)

Compressed Sensing MRI With Phase Noise Disturbance Based on Adaptive Tight Frame and Total Variation. - Fan Xiaoyu; Qiusheng Lian; Baoshun Shi (IEEE Access, 2017)

Transfer orthogonal sparsifying transform learning for phase retrieval. - Qiusheng Lian; Baoshun Shi; Shuzhen Chen 002 (Digit. Signal Process., 2017)

SBM3D: Sparse Regularization Model Induced by BM3D for Weighted Diffraction Imaging. - Baoshun Shi; Qiusheng Lian; Shuzhen Chen 002; Fan Xiaoyu (IEEE Access, 2018)

FASPR: A fast sparse phase retrieval algorithm via the epigraph concept. - Baoshun Shi; Shuzhen Chen 002; Ye Tian 014; Fan Xiaoyu; Qiusheng Lian (Digit. Signal Process., 2018)

Coded diffraction imaging via double sparse regularization model. - Baoshun Shi; Qiusheng Lian; Shuzhen Chen 002; Ye Tian 014; Fan Xiaoyu (Digit. Signal Process., 2018)

Multi-scale Cross-path Concatenation Residual Network for Poisson denoising. - Yueming Su; Qiusheng Lian; Xiaohua Zhang; Baoshun Shi; Fan Xiaoyu (IET Image Process., 2019)

PPR: Plug-and-play regularization model for solving nonlinear imaging inverse problems. - Baoshun Shi; Qiusheng Lian; Fan Xiaoyu (Signal Process., 2019)

Compressed sensing MRI based on the hybrid regularization by denoising and the epigraph projection. - Qiusheng Lian; Fan Xiaoyu; Baoshun Shi; Xiaohua Zhang (Signal Process., 2020)

Deep prior-based sparse representation model for diffraction imaging: A plug-and-play method. - Baoshun Shi; Qiusheng Lian; Huibin Chang (Signal Process., 2020)

A Trainable Bounded Denoiser Using Double Tight Frame Network for Snapshot Compressive Imaging. - Baoshun Shi; Yuxin Wang; Qiusheng Lian (ICASSP, 2022)

Convolutional Sparse Coding with Weighted L1 Norm for Phase Retrieval: Algorithm and Its Deep Unfolded Network. - Baoshun Shi; Yating Gao; Ke Jiang; Qiusheng Lian (ICIP, 2022)

TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction. - Baoshun Shi; Ke Jiang; Shaolei Zhang; Qiusheng Lian; Yanwei Qin (CoRR, 2022)

Supervised dual tight frame learning with deep thresholding network for phase retrieval. - Baoshun Shi; Qiusheng Lian; Yueming Su (IET Image Process., 2022)

DualPRNet: Deep Shrinkage Dual Frame Network for Deep Unrolled Phase Retrieval. - Baoshun Shi; Qiusheng Lian (IEEE Signal Process. Lett., 2022)

LG-Net: Local and global complementary priors induced multi-stage progressive network for compressed sensing. - Qiusheng Lian; Yueming Su; Baoshun Shi; Dan Zhang (Signal Process., 2023)

Provable General Bounded Denoisers for Snapshot Compressive Imaging With Convergence Guarantee. - Baoshun Shi; Yuxin Wang; Dan Li (IEEE Trans. Computational Imaging, 2023)

DeepCDL-PR: Deep unfolded convolutional dictionary learning with weighted ℓ1-norm for phase retrieval. - Baoshun Shi; Yating Gao; Yueming Su; Qiusheng Lian (Digit. Signal Process., 2023)

Bayesian self-supervised learning allying with Transformer powered compressed sensing imaging. - Yueming Su; Yongli Yang; Baoshun Shi; Yuwei Zhang (Digit. Signal Process., 2023)

Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging with Convergence Analysis. - Baoshun Shi; Kexun Liu (IEEE CAA J. Autom. Sinica, 2023)

BGFlow: Brightness-guided normalizing flow for low-light image enhancement. - Jiale Chen; Qiusheng Lian; Baoshun Shi (Displays, 2024)

Transformer based Douglas-Rachford unrolling network for compressed sensing. - Yueming Su; Qiusheng Lian; Dan Zhang; Baoshun Shi (Signal Process. Image Commun., 2024)

Artifact Region-Aware Transformer: Global Context Helps CT Metal Artifact Reduction. - Baoshun Shi; Shaolei Zhang; Fu ZhaoRan (IEEE Signal Process. Lett., 2024)

Coupling Model- and Data-Driven Networks for CT Metal Artifact Reduction. - Baoshun Shi; Shaolei Zhang; Ke Jiang; Qiusheng Lian (IEEE Trans. Computational Imaging, 2024)

Cartoon-texture guided network for low-light image enhancement. - Baoshun Shi; Chunzi Zhu; Lingyan Li; Huagui Huang (Digit. Signal Process., 2024)

Provable deep video denoiser using spatial-temporal information for video snapshot compressive imaging: Algorithm and convergence analysis. - Baoshun Shi; Dan Li; Yuxin Wang; Yueming Su; Qiusheng Lian (Signal Process., 2024)

Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography. - Baoshun Shi; Ke Jiang; Shaolei Zhang; Qiusheng Lian; Yanwei Qin; Yunsong Zhao (Mach. Learn. Sci. Technol., 2024)

A Two-Phase Reference-Free Approach for Low-Light Image Enhancement. - Jiale Chen; Qiusheng Lian; Baoshun Shi; Chengli Gao (Circuits Syst. Signal Process., 2024)

Self-Supervised Normalizing Flow for Jointing Low-Light Enhancement and Deblurring. - Lingyan Li; Chunzi Zhu; Jiale Chen; Baoshun Shi; Qiusheng Lian (Circuits Syst. Signal Process., 2024)

SAM-guided complementary prior network for compressed sensing MRI. - Yueming Su; Zheng Liu; Baoshun Shi (Digit. Signal Process., 2025)

Unsupervised learning-based deep sparsifying transform network for joint CT metal artifact reduction and super-resolution reconstruction. - Shengnan Yan; Yingshuai Zhao; Baoshun Shi; Yueming Su (Digit. Signal Process., 2025)

OptNet: Optimization-inspired network beyond deep unfolding for structural artifact reduction. - Ke Jiang; Yingshuai Zhao; Baoshun Shi (Knowl. Based Syst., 2025)

Provably bounded prompting prior network for universal compressed sensing magnetic resonance imaging. - Baoshun Shi; Zheng Liu; Kexun Liu; Yueming Su (Knowl. Based Syst., 2025)

UPrime: Unrolled Phase Retrieval Iterative Method with provable convergence. - Baoshun Shi; Yating Gao; Runze Zhang (Signal Process., 2025)

APG-Net: APG Optimization-Induced Multiscale Attention Network for MRI Reconstruction From Undersampled k-Space Measurements. - Hongping Gan; Zitong Zhao; Yunong Chen; Shanshan Shan; Baoshun Shi; Chunyi Liu (IEEE Trans. Instrum. Meas., 2025)

Prompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR. - Baoshun Shi; Bing Chen; Shaolei Zhang; Huazhu Fu; Zhanli Hu (CoRR, 2025)

Visual and text prompts guided interpretable network for universal low-dose CT MAR. - Baoshun Shi; Bing Chen; Shaolei Zhang; Ke Jiang; Shuo Liu (Pattern Recognit., 2026)