3 papers
eess.IV2024
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
Shijun Liang, Evan Bell, Avrajit Ghosh +1
Deep learning methods are highly effective for many image reconstruction tasks. However, the performance of supervised learned models can degrade when applied to distinct experimen…
cs.CV2024
Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction
Shijun Liang, Evan Bell, Qing Qu +2
The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medi…
eess.IV2023
SMUG: Towards robust MRI reconstruction by smoothed unrolling
Hui Li, Jinghan Jia, Shijun Liang +3
Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be…