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

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Each tutorial focuses on a specific submodule of the full pipeline.

* :ref:`Tutorial 1 <sphx_glr_gallery_tuto_01_a_acquisition_operators.py>`.a introduces the basics of measurement operators.

* :ref:`Tutorial 1 <sphx_glr_gallery_tuto_01_b_splitting.py>`.b introduces the splitting of measurement operators.

* :ref:`Tutorial 1 <sphx_glr_gallery_tuto_01_c_HadamSplit2d.py>`.c introduces the 2d Hadamard transform with subsampling.

* :ref:`Tutorial 2 <sphx_glr_gallery_tuto_02_noise.py>` introduces the noise operators.

* :ref:`Tutorial 3 <sphx_glr_gallery_tuto_03_pseudoinverse_linear.py>` demonstrates pseudo-inverse reconstructions from Hadamard 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 3 <sphx_glr_gallery_tuto_03_pseudoinverse_cnn_linear.py>` uses a CNN to denoise the image if necessary

* :ref:`Tutorial 4 <sphx_glr_gallery_tuto_04_train_pseudoinverse_cnn_linear.py>` is used to train the CNN introduced in Tutorial 3

* :ref:`Tutorial 5 <sphx_glr_gallery_tuto_05_acquisition_split_measurements.py>` introduces a new type of measurement operator ('split') that simulates positive and negative measurements

* :ref:`Tutorial 6 <sphx_glr_gallery_tuto_06_dcnet_split_measurements.py>` uses a Denoised Completion Network with a trainable image denoiser to improve the results obtained in Tutorial 5

* :ref:`Tutorial 7 <sphx_glr_gallery_tuto_07_drunet_split_measurements.py>` shows how to perform image reconstruction using a pretrained plug-and-play denoising network.

* :ref:`Tutorial 8 <sphx_glr_gallery_tuto_08_lpgd_split_measurements.py>` shows how to perform image reconstruction using a learnt proximal gradient descent.

* :ref:`Tutorial 9 <sphx_glr_gallery_tuto_09_dynamic.py>` explains motion simulation from an image, dynamic measurements and reconstruction.

* Explore :ref:`Bonus Tutorial <sphx_glr_gallery_tuto_bonus_advanced_methods_colab.py>` if you want to go deeper into Spyrit's capabilities


.. _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