294 citations · 928 across the 63 of their papers we have counts for
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Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography
Michael T. McCann, Marc L. Klasky, Jennifer L. Schei +1
We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a train…
Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
Xikai Yang, Yong Long, Saiprasad Ravishankar
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings. Model-based image reconstruction methods ha…
Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction
Xikai Yang, Yong Long, Saiprasad Ravishankar
Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, com…
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
Siqi Ye, Zhipeng Li, Michael T. McCann +2
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typ…
Supervised Learning of Sparsity-Promoting Regularizers for Denoising
Michael T. McCann, Saiprasad Ravishankar
We present a method for supervised learning of sparsity-promoting regularizers for image denoising. Sparsity-promoting regularization is a key ingredient in solving modern image re…
Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration
Lanqing Guo, Zhiyuan Zha, Saiprasad Ravishankar +1
Constructing effective image priors is critical to solving ill-posed inverse problems in image processing and imaging. Recent works proposed to exploit image non-local similarity f…
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction
Xikai Yang, Xuehang Zheng, Yong Long +1
Signal models based on sparse representation have received considerable attention in recent years. Compared to synthesis dictionary learning, sparsifying transform learning involve…