Deep Unrolling & Model-Based Learning
The papers in this topic, the datasets they use, the teams that focus on it, the toolkits assigned to it, and the authors with several papers here. Select any node to see its connections.
The interactive graph could not be loaded (JavaScript or the graph library is unavailable); the same entries are listed below as links.
Deep Unrolling & Model-Based Learning
Team
- Computational Image Analysis (CIA) Group
- Fessler Group
- Mathematical Imaging Group (Ozan Öktem)
- Pattern Recognition Lab (LME)
Topic
Paper
- A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and High-Quality Industrial Cone-beam CT Reconstruction
- A review on deep learning in medical image reconstruction
- ACDNet
- ACID-A
- ACID-B
- ADMM-based deep reconstruction for limited-angle CT
- AirNet
- AUTOMAP
- BCD-Net
- CasRedSCAN
- CG-GLORE
- CNNRPGD
- CoCo-PnP
- Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
- CTO
- DAN-Net
- Data-Efficient Limited-Angle CT Using Deep Priors and Regularization
- DD-Net
- DDCL
- DDN-FM
- DeACNN
- DEAR
- DeepCNN
- DICDNet
- DIOR
- DL-InvProb
- Dose-Aware Cold Diffusion with Physics Consistency for Generalizable Low-Dose CT Reconstruction
- DREAM-Net
- DRONE
- DSE
- DSigNet
- Dual-domain U-Nets with embedded back projection operators for motion-resolved 4D CBCT reconstruction
- EAGLE
- Efficient Unrolled Networks for Large-Scale 3D Inverse Problems
- End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT
- Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data
- ETEDN
- FIND-Net -- Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction
- FISTA-Net
- FreeSeed
- GB-SVFBP
- HARU-Net
- HD-CNN
- IDOL-Net
- InDuDoNet
- iRadonMAP
- Known Operator Learning Enables Constrained Projection Geometry Conversion: Parallel to Cone-Beam for Hybrid MR/X-Ray Imaging
- LAMA
- LEARN
- LEARN++
- Learned convex regularizers for inverse problems
- Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography
- Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction
- LPD
- LUCID
- MetaInv-Net
- MInDI-3D
- MoDL
- Momentum-Net
- Multi-Scale Learned Iterative Reconstruction
- OSCNet
- Plug-and-Play Learned Proximal Trajectory for 3D Sparse-View X-Ray Computed Tomography
- PTPN
- RAM
- SACNN
- Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction
- SISVM
- SOUL-Net
- SPECIAL
- SPIE-DIR
- SPSS
- Tensor-Net
- Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
- VISS
Toolkit
- DeepInverse
- diffct
- Learned Gradient Tomography
- Learned Primal-Dual
- LION
- MIRTorch
- PYRO-NN
- tomosipo
- torch-radon
Dataset
Benchmark
Author
- Amirkoushyar Ziabari
- Andreas Maier
- Chuang Niu
- Daniel Barco
- Deyu Meng
- Dianlin Hu
- Dong Liang
- Dong Zeng
- Frank-Peter Schilling
- Ge Wang
- Guotao Quan
- Haimiao Zhang
- Hairong Zheng
- Hengyong Yu
- Hong Wang
- Hongming Shan
- Hu Chen
- Huaiqiang Sun
- Ivo Herzig
- Jeffrey A. Fessler
- Ji He
- Jianhua Ma
- Jiliu Zhou
- Jin Liu
- Kai Ma
- Linda-Sophie Schneider
- Mareike Thies
- Obaidullah Rahman
- Ozan Öktem
- Pascal Paysan
- Pingkun Yan
- Romain Vo
- Shaoyu Wang
- Shouhua Luo
- Tao Wang
- Varut Vardhanabhuti
- Weiwen Wu
- Wenjun Xia
- Wenxiang Cong
- Xi Tao
- Yan Liu
- Yang Chen
- Yefeng Zheng
- Yi Zhang
- Yikun Zhang
- Yipeng Sun
- Yongbo Wang
- Yongqiang Huang
- Yuexiang Li
- Zexin Lu
- Zhanli Hu
- Ziyuan Yang