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Found 5 papers, 1 papers with code
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis.
Momentum-Net for Low-Dose CT Image Reconstruction
We also investigated the spectral normalization technique that applies to image refining NN learning to satisfy the nonexpansive NN property; however, experimental results show that this does not improve the image reconstruction performance of Momentum-Net.
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms.
SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models
The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
Signal Processing Image and Video Processing Optimization and Control Medical Physics
