Contents Menu Expand Light mode Dark mode Auto light/dark, in light mode Auto light/dark, in dark mode Skip to content
SCICO 0.0.8.dev0+e6fb9fc documentation
Logo
0.0.8.dev0+e6fb9fc

User Documentation

  • Overview
  • Inverse Problems
  • Why SCICO?
  • Installing SCICO
  • Main SCICO Classes
  • Notes
  • Usage Examples
    • TV-Regularized Abel Inversion
    • Parameter Tuning for TV-Regularized Abel Inversion
    • TV-Regularized Cone Beam CT for Symmetric Objects
    • CT Reconstruction with CG and PCG
    • 3D TV-Regularized Sparse-View CT Reconstruction (ADMM Solver)
    • 3D TV-Regularized Sparse-View CT Reconstruction (Proximal ADMM Solver)
    • TV-Regularized Sparse-View CT Reconstruction (Integrated Projector)
    • TV-Regularized Sparse-View CT Reconstruction (ASTRA Projector)
    • TV-Regularized Sparse-View CT Reconstruction (Multiple Projectors)
    • 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)
    • CT Training and Reconstruction with MoDL
    • CT Training and Reconstruction with ODP
    • CT Training and Reconstructions with UNet
    • 2D X-ray Transform Comparison
    • 3D X-ray Transform Comparison
    • 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
    • β„“1 Total Variation Denoising
    • Polar Total Variation Denoising (PDHG)
    • Total Variation Denoising (ADMM)
    • Total Variation Denoising with Constraint (APGM)
    • Comparison of Optimization Algorithms for Total Variation Denoising
    • Denoising with Approximate Total Variation Proximal Operator
    • Complex Total Variation Denoising with NLPADMM Solver
    • Complex Total Variation Denoising with PDHG Solver
    • Comparison of DnCNN Variants for Image Denoising
    • 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)
    • PPP (with BM3D) Image Demosaicing
    • PPP (with DnCNN) Image Superresolution
    • 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
    • PPP (with BM3D) CT Reconstruction (ADMM with CG Subproblem Solver)
    • PPP (with BM3D) CT Reconstruction (ADMM with Fast SVMBIR Prox)
    • 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
    • TV-Regularized Abel Inversion
    • Parameter Tuning for TV-Regularized Abel Inversion
    • TV-Regularized Cone Beam CT for Symmetric Objects
    • TV-Regularized Sparse-View CT Reconstruction (Integrated Projector)
    • TV-Regularized Sparse-View CT Reconstruction (Multiple Projectors)
    • TV-Regularized Sparse-View CT Reconstruction (ASTRA Projector)
    • 3D TV-Regularized Sparse-View CT Reconstruction (ADMM Solver)
    • 3D TV-Regularized Sparse-View CT Reconstruction (Proximal ADMM Solver)
    • 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)
    • β„“1 Total Variation Denoising
    • Polar Total Variation Denoising (PDHG)
    • Total Variation Denoising (ADMM)
    • Total Variation Denoising with Constraint (APGM)
    • Comparison of Optimization Algorithms for Total Variation Denoising
    • Denoising with Approximate Total Variation Proximal Operator
    • Complex Total Variation Denoising with NLPADMM Solver
    • Complex Total Variation Denoising with PDHG Solver
    • TV-Regularized 3D DiffuserCam Reconstruction
    • 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
    • CT Data Generation for NN Training
    • CT Training and Reconstruction with MoDL
    • CT Training and Reconstruction 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
    • TV-Regularized Abel Inversion
    • Parameter Tuning for TV-Regularized Abel Inversion
    • TV-Regularized Cone Beam CT for Symmetric Objects
    • TV-Regularized Sparse-View CT Reconstruction (ASTRA Projector)
    • TV-Regularized Sparse-View CT Reconstruction (Integrated Projector)
    • 3D TV-Regularized Sparse-View CT Reconstruction (ADMM Solver)
    • TV-Regularized Low-Dose CT Reconstruction
    • TV-Regularized Sparse-View CT Reconstruction (Multiple Projectors)
    • 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)
    • 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
    • β„“1 Total Variation Denoising
    • Total Variation Denoising (ADMM)
    • Comparison of Optimization Algorithms for Total Variation Denoising
    • Denoising with Approximate Total Variation Proximal Operator
    • Video Decomposition via Robust PCA
    • TV-Regularized CT Reconstruction (Multiple Algorithms)
    • Comparison of Optimization Algorithms for Total Variation Denoising
    • 3D TV-Regularized Sparse-View CT Reconstruction (Proximal ADMM Solver)
    • Image Deconvolution with TV Regularization (Proximal ADMM Solver)
    • Comparison of Optimization Algorithms for Total Variation Denoising
    • PPP (with DnCNN) Image Deconvolution (Proximal ADMM Solver)
    • Complex Total Variation Denoising with NLPADMM Solver
    • TV-Regularized CT Reconstruction (Multiple Algorithms)
    • Polar Total Variation Denoising (PDHG)
    • Comparison of Optimization Algorithms for Total Variation Denoising
    • Complex Total Variation Denoising with PDHG Solver
    • PPP (with BM3D) Image Deconvolution (APGM Solver)
    • Basis Pursuit DeNoising (APGM)
    • Non-Negative Basis Pursuit DeNoising (APGM)
    • Non-negative Poisson Loss Reconstruction (APGM)
    • Total Variation Denoising with Constraint (APGM)
    • Denoising with Approximate Total Variation Proximal Operator
    • CT Reconstruction with CG and PCG
  • API Reference
    • scico.data
    • scico.denoiser
    • scico.diagnostics
    • scico.examples
    • scico.flax
      • scico.flax.blocks
      • scico.flax.examples
        • scico.flax.examples.data_generation
        • scico.flax.examples.data_preprocessing
        • scico.flax.examples.examples
        • scico.flax.examples.typed_dict
      • scico.flax.inverse
      • scico.flax.train
        • scico.flax.train.apply
        • scico.flax.train.checkpoints
        • scico.flax.train.clu_utils
        • scico.flax.train.diagnostics
        • scico.flax.train.input_pipeline
        • scico.flax.train.learning_rate
        • scico.flax.train.losses
        • scico.flax.train.spectral
        • scico.flax.train.state
        • scico.flax.train.steps
        • scico.flax.train.trainer
        • scico.flax.train.traversals
        • scico.flax.train.typed_dict
    • scico.function
    • scico.functional
    • scico.linop
      • scico.linop.optics
      • scico.linop.xray
        • scico.linop.xray.abel
        • scico.linop.xray.astra
        • scico.linop.xray.svmbir
        • scico.linop.xray.symcone
    • scico.loss
    • scico.metric
    • scico.numpy
      • scico.numpy.fft
      • scico.numpy.linalg
      • scico.numpy.testing
      • scico.numpy.util
    • scico.operator
      • scico.operator.biconvolve
    • scico.optimize
      • scico.optimize.admm
      • scico.optimize.pgm
    • scico.plot
    • scico.random
    • scico.ray
      • scico.ray.tune
    • scico.scipy
      • scico.scipy.special
    • scico.solver
    • scico.trace
    • scico.typing
    • scico.util
  • References

Developer Documentation

  • Developers
  • Contributing
  • Style Guide
Back to top

scico.flax.examplesΒΆ

Data utility functions used by Flax example scripts.

Modules

scico.flax.examples.data_generation

Functionality to generate training data for Flax example scripts.

scico.flax.examples.data_preprocessing

Image manipulation utils.

scico.flax.examples.examples

Generation and loading of data used in Flax example scripts.

scico.flax.examples.typed_dict

Definition of typed dictionaries for training data.

Next
scico.flax.examples.data_generation
Previous
scico.flax.blocks
Copyright © 2020-2026, SCICO Developers
Made with Sphinx and @pradyunsg's Furo