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Found 8 papers, 1 papers with code
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction
Signal models based on sparse representation have received considerable attention in recent years.
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Numerical results with phantom data show that applying faster numerical solvers to model-based image reconstruction (MBIR) modules of BCD-Net leads to faster and more accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy, compared to the state-of-the-art MBIR method using learned transforms; BCD-Net achieves better image quality, compared to a state-of-the-art iterative NN architecture, ADMM-Net.
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computational imaging applications.
Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform
Our results with the extended cardiac-torso (XCAT) phantom data and clinical chest data show that, for sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST-$\ell_1$ improves the quality of reconstructed images compared to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$ prior with learned ST.
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images.
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.
