Usage Examples#
Example Dependencies#
Some examples use additional dependencies, which are listed in examples_requirements.txt.
The additional requirements should be installed via pip, with the exception of astra-toolbox
,
which should be installed via conda:
conda install -c astra-toolbox astra-toolbox
pip install -r examples/examples_requirements.txt # Installs other example requirements
The dependencies can also be installed individually as required.
Note that astra-toolbox
should be installed on a host with one or more CUDA GPUs to ensure
that the version with GPU support is installed.
Run Time#
Most of these examples have been constructed with sufficiently small test problems to allow them to run to completion within 5 minutes or less on a reasonable workstation. Note, however, that it was not feasible to construct meaningful examples of the training of some of the deep learning algorithms that complete within a relatively short time; the examples “CT Training and Reconstructions with MoDL” and “CT Training and Reconstructions with ODP” in particular are much slower, and can require multiple hours to run on a workstation with multiple GPUs.
Organized by Application#
Computed Tomography#
- TV-Regularized Abel Inversion
- Parameter Tuning for TV-Regularized Abel Inversion
- CT Reconstruction with CG and PCG
- 3D TV-Regularized Sparse-View CT Reconstruction
- TV-Regularized Sparse-View CT Reconstruction (ASTRA Projector)
- TV-Regularized Sparse-View CT Reconstruction (Integrated Projector)
- TV-Regularized Low-Dose CT Reconstruction
- TV-Regularized CT Reconstruction (Multiple Algorithms)
- PPP (with BM3D) CT Reconstruction (ADMM with CG Subproblem Solver)
- PPP (with BM3D) CT Reconstruction (ADMM with Fast SVMBIR Prox)
- PPP (with BM3D) Fan-Beam CT Reconstruction
- CT Training and Reconstructions with MoDL
- CT Training and Reconstructions with ODP
- CT Training and Reconstructions with UNet
- X-ray Transform Comparison
- TV-Regularized Sparse-View CT Reconstruction (Multiple Projectors, Common Sinogram)
- TV-Regularized Sparse-View CT Reconstruction (Multiple Projectors)
Deconvolution#
- Circulant Blur Image Deconvolution with TV Regularization
- Image Deconvolution with TV Regularization (ADMM Solver)
- Image Deconvolution with TV Regularization (Proximal ADMM Solver)
- Parameter Tuning for Image Deconvolution with TV Regularization (ADMM Solver)
- Deconvolution Microscopy (Single Channel)
- Deconvolution Microscopy (All Channels)
- PPP (with BM3D) Image Deconvolution (ADMM Solver)
- PPP (with BM3D) Image Deconvolution (APGM Solver)
- PPP (with DnCNN) Image Deconvolution (ADMM Solver)
- PPP (with DnCNN) Image Deconvolution (Proximal ADMM Solver)
- PPP (with BM4D) Volume Deconvolution
- Deconvolution Training and Reconstructions with MoDL
- Deconvolution Training and Reconstructions with ODP
Sparse Coding#
Miscellaneous#
- PPP (with BM3D) Image Demosaicing
- PPP (with DnCNN) Image Superresolution
- ℓ1 Total Variation Denoising
- Total Variation Denoising (ADMM)
- Total Variation Denoising with Constraint (APGM)
- Comparison of Optimization Algorithms for Total Variation Denoising
- Complex Total Variation Denoising with NLPADMM Solver
- Complex Total Variation Denoising with PDHG Solver
- Comparison of DnCNN Variants for Image Denoising
- TV-Regularized 3D DiffuserCam Reconstruction
- Video Decomposition via Robust PCA
- CT Data Generation for NN Training
- Blurred Data Generation (Natural Images) for NN Training
- Blurred Data Generation (Foams) for NN Training
- Noisy Data Generation for NN Training
Organized by Regularization#
Plug and Play Priors#
- PPP (with BM3D) CT Reconstruction (ADMM with CG Subproblem Solver)
- PPP (with BM3D) CT Reconstruction (ADMM with Fast SVMBIR Prox)
- PPP (with BM3D) Fan-Beam CT Reconstruction
- PPP (with BM3D) Image Deconvolution (ADMM Solver)
- PPP (with BM3D) Image Deconvolution (APGM Solver)
- PPP (with DnCNN) Image Deconvolution (ADMM Solver)
- PPP (with DnCNN) Image Deconvolution (Proximal ADMM Solver)
- PPP (with BM4D) Volume Deconvolution
- PPP (with BM3D) Image Demosaicing
- PPP (with DnCNN) Image Superresolution
Total Variation#
- TV-Regularized Abel Inversion
- Parameter Tuning for TV-Regularized Abel Inversion
- TV-Regularized Sparse-View CT Reconstruction (ASTRA Projector)
- TV-Regularized Sparse-View CT Reconstruction (Integrated Projector)
- 3D TV-Regularized Sparse-View CT Reconstruction
- TV-Regularized Low-Dose CT Reconstruction
- TV-Regularized CT Reconstruction (Multiple Algorithms)
- Circulant Blur Image Deconvolution with TV Regularization
- Image Deconvolution with TV Regularization (ADMM Solver)
- Parameter Tuning for Image Deconvolution with TV Regularization (ADMM Solver)
- Image Deconvolution with TV Regularization (Proximal ADMM Solver)
- Deconvolution Microscopy (Single Channel)
- Deconvolution Microscopy (All Channels)
- ℓ1 Total Variation Denoising
- Total Variation Denoising (ADMM)
- Total Variation Denoising with Constraint (APGM)
- Comparison of Optimization Algorithms for Total Variation Denoising
- Complex Total Variation Denoising with NLPADMM Solver
- Complex Total Variation Denoising with PDHG Solver
- TV-Regularized 3D DiffuserCam Reconstruction
Sparsity#
- TV-Regularized 3D DiffuserCam Reconstruction
- Non-Negative Basis Pursuit DeNoising (ADMM)
- Non-Negative Basis Pursuit DeNoising (APGM)
- Convolutional Sparse Coding (ADMM)
- Convolutional Sparse Coding with Mask Decoupling (ADMM)
- Basis Pursuit DeNoising (APGM)
- Non-negative Poisson Loss Reconstruction (APGM)
- Video Decomposition via Robust PCA
Machine Learning#
- CT Data Generation for NN Training
- CT Training and Reconstructions with MoDL
- CT Training and Reconstructions with ODP
- CT Training and Reconstructions with UNet
- Blurred Data Generation (Natural Images) for NN Training
- Blurred Data Generation (Foams) for NN Training
- Deconvolution Training and Reconstructions with MoDL
- Deconvolution Training and Reconstructions with ODP
- Noisy Data Generation for NN Training
- Training of DnCNN for Denoising
- Comparison of DnCNN Variants for Image Denoising
Organized by Optimization Algorithm#
ADMM#
- TV-Regularized Abel Inversion
- Parameter Tuning for TV-Regularized Abel Inversion
- TV-Regularized Sparse-View CT Reconstruction (ASTRA Projector)
- TV-Regularized Sparse-View CT Reconstruction (Integrated Projector)
- 3D TV-Regularized Sparse-View CT Reconstruction
- TV-Regularized Low-Dose CT Reconstruction
- TV-Regularized CT Reconstruction (Multiple Algorithms)
- PPP (with BM3D) CT Reconstruction (ADMM with CG Subproblem Solver)
- PPP (with BM3D) CT Reconstruction (ADMM with Fast SVMBIR Prox)
- PPP (with BM3D) Fan-Beam CT Reconstruction
- Circulant Blur Image Deconvolution with TV Regularization
- Image Deconvolution with TV Regularization (ADMM Solver)
- Parameter Tuning for Image Deconvolution with TV Regularization (ADMM Solver)
- Deconvolution Microscopy (Single Channel)
- Deconvolution Microscopy (All Channels)
- PPP (with BM3D) Image Deconvolution (ADMM Solver)
- PPP (with DnCNN) Image Deconvolution (ADMM Solver)
- PPP (with BM4D) Volume Deconvolution
- TV-Regularized 3D DiffuserCam Reconstruction
- Non-Negative Basis Pursuit DeNoising (ADMM)
- Convolutional Sparse Coding (ADMM)
- Convolutional Sparse Coding with Mask Decoupling (ADMM)
- PPP (with BM3D) Image Demosaicing
- PPP (with DnCNN) Image Superresolution
- ℓ1 Total Variation Denoising
- Total Variation Denoising (ADMM)
- Comparison of Optimization Algorithms for Total Variation Denoising
- Video Decomposition via Robust PCA