11 citations · 24 across the 6 of their papers we have counts for
6 papers
Converged Deep Framework Assembling Principled Modules for CS-MRI
Risheng Liu, Yuxi Zhang, Shichao Cheng +2
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR data acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for…
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Risheng Liu, Yuxi Zhang, Shichao Cheng +2
Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limi…
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems
Risheng Liu, Shichao Cheng, Yi He +3
Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have b…
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond
Risheng Liu, Yi He, Shichao Cheng +2
Blind image deblurring plays a very important role in many vision and multimedia applications. Most existing works tend to introduce complex priors to estimate the sharp image stru…
Toward Designing Convergent Deep Operator Splitting Methods for Task-specific Nonconvex Optimization
Risheng Liu, Shichao Cheng, Yi He +2
Operator splitting methods have been successfully used in computational sciences, statistics, learning and vision areas to reduce complex problems into a series of simpler subprobl…
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
Risheng Liu, Xin Fan, Shichao Cheng +2
Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus lack of rigorous m…