447 citations · 782 across the 31 of their papers we have counts for
33 papers
Transferable Optimization Network for Cross-Domain Image Reconstruction
Yunmei Chen, Chi Ding, Xiaojing Ye
We develop a novel transfer learning framework to tackle the challenge of limited training data in image reconstruction problems. The proposed framework consists of two training st…
LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstruction
Chi Ding, Qingchao Zhang, Ge Wang +2
We propose a learnable variational model that learns the features and leverages complementary information from both image and measurement domains for image reconstruction. In parti…
LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT
Chi Ding, Qingchao Zhang, Ge Wang +2
Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block opt…
High-dimensional Optimal Density Control with Wasserstein Metric Matching
Shaojun Ma, Mengxue Hou, Xiaojing Ye +1
We present a novel computational framework for density control in high-dimensional state spaces. The considered dynamical system consists of a large number of indistinguishable age…
Parameterized Wasserstein Hamiltonian Flow
Hao Wu, Shu Liu, Xiaojing Ye +1
In this work, we propose a numerical method to compute the Wasserstein Hamiltonian flow (WHF), which is a Hamiltonian system on the probability density manifold. Many well-known PD…
Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction
Chi Ding, Qingchao Zhang, Ge Wang +2
We propose a novel Learned Alternating Minimization Algorithm (LAMA) for dual-domain sparse-view CT image reconstruction. LAMA is naturally induced by a variational model for CT re…