1 citations · 1 across the 6 of their papers we have counts for
6 papers
Learning Scan-Adaptive MRI Undersampling Patterns with Pre-Optimized Mask Supervision
Aryan Dhar, Siddhant Gautam, Saiprasad Ravishankar
Deep learning techniques have gained considerable attention for their ability to accelerate MRI data acquisition while maintaining scan quality. In this work, we present a convolut…
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…
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…
Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning
Ling Chen, Zhishen Huang, Yong Long +1
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image recons…
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…
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration
Chong Wang, Rongkai Zhang, Saiprasad Ravishankar +1
Image restoration schemes based on the pre-trained deep models have received great attention due to their unique flexibility for solving various inverse problems. In particular, th…