:orphan: Tutorials ========= This series of tutorials should guide you through the use of the SPyRiT pipeline. .. figure:: ../fig/direct_net.png :width: 600 :align: center :alt: SPyRiT pipeline | Each tutorial focuses on a specific submodule of the full pipeline. * :ref:`Tutorial 1 `.a introduces the basics of measurement operators. * :ref:`Tutorial 1 `.b introduces the splitting of measurement operators. * :ref:`Tutorial 1 `.c introduces the 2d Hadamard transform with subsampling. * :ref:`Tutorial 2 ` introduces the noise operators. * :ref:`Tutorial 3 ` demonstrates pseudo-inverse reconstructions from Hadamard measurements. * :ref:`Tutorial 4 `.a introduces data-driven post-processing reconstruction. * :ref:`Tutorial 4 `.b trains the post-processing CNN used in :ref:`Tutorial 4 `.a. * :ref:`Tutorial 5 ` introduces the denoised completion network for the reconstruction of Poisson-corrupted subsampled measurements. .. note:: The Python script (*.py*) or Jupyter notebook (*.ipynb*) corresponding to each tutorial can be downloaded at the bottom of the page. The images used in these files can be found on `GitHub`_. The tutorials below will gradually be updated to be compatible with SPyRiT 3 (work in progress, in the meantime see SPyRiT `2.4.0`_). * :ref:`Tutorial 6 ` uses a Denoised Completion Network with a trainable image denoiser to improve the results obtained in Tutorial 5 * :ref:`Tutorial 7 ` shows how to perform image reconstruction using a pretrained plug-and-play denoising network. * :ref:`Tutorial 8 ` shows how to perform image reconstruction using a learnt proximal gradient descent. * :ref:`Tutorial 9 ` explains motion simulation from an image, dynamic measurements and reconstruction. .. _GitHub: https://github.com/openspyrit/spyrit/tree/3895b5e61fb6d522cff5e8b32a36da89b807b081/tutorial/images/test .. _2.4.0: https://spyrit.readthedocs.io/en/2.4.0/gallery/index.html .. raw:: html
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01.a. Acquisition operators (basic)
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01.b. Acquisition operators (splitting)
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01.c. Acquisition operators (HadamSplit2d)
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02. Noise operators
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03. Pseudoinverse solution from linear measurements
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04.a. Pseudoinverse + CNN (reconstruction)
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04.b. Pseudoinverse + CNN (training)
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05. Denoised Completion Network (DC-Net)
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.. toctree:: :hidden: /gallery/tuto_01_a_acquisition_operators /gallery/tuto_01_b_splitting /gallery/tuto_01_c_HadamSplit2d /gallery/tuto_02_noise /gallery/tuto_03_pseudoinverse_linear /gallery/tuto_04_pseudoinverse_cnn_linear /gallery/tuto_04_b_train_pseudoinverse_cnn_linear /gallery/tuto_05_dcnet .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_