1 citations · 1 across the 2 of their papers we have counts for
3 papers
eess.IV2026
A deep dictionary network-based foundation model for ultra-low-dose CT denoising
Baoshun Shi, Shuangyi Yang, Ke Jiang +3
Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality. Existing deep learning-based denoising m…
cs.CV2025★ 1 cited
Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstruction
Baoshun Shi, Ke Jiang, Qiusheng Lian +2
Despite significant advancements in deep learning-based sparse-view computed tomography (SVCT) reconstruction algorithms, these methods still encounter two primary limitations: (i)…
cs.NI2022
TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction
Baoshun Shi, Ke Jiang, Shaolei Zhang +2
Recent deep learning-based methods have achieved promising performance for computed tomography metal artifact reduction (CTMAR). However, most of them suffer from two limitations:…