294 citations · 928 across the 63 of their papers we have counts for
7 papers · 1 filter
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…
Learning Sparsity-Promoting Regularizers using Bilevel Optimization
Avrajit Ghosh, Michael T. McCann, Madeline Mitchell +1
We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images. Sparsity-promoting regularization is a key ingredient in solving mo…
Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data
Shijun Liang, Anish Lahiri, Saiprasad Ravishankar
Recent medical image reconstruction techniques focus on generating high-quality medical images suitable for clinical use at the lowest possible cost and with the fewest possible ad…
Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction
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…
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction
Xikai Yang, Zhishen Huang, Yong Long +1
The recently proposed sparsifying transform models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure revea…
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing
Zhiyuan Zha, Bihan Wen, Xin Yuan +3
The compressive sensing (CS) scheme exploits much fewer measurements than suggested by the Nyquist-Shannon sampling theorem to accurately reconstruct images, which has attracted co…
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
Anish Lahiri, Marc Klasky, Jeffrey A. Fessler +1
Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the num…