.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_image_restoration.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_image_restoration.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_image_restoration.py:


Image Restoration: ProxTorch Logo via TV and TV-L1 Regularization
==================================================================

Using ProxTorch's TV_2D and TVL1_2D operators, this example demonstrates image restoration
of a noisy ProxTorch logo through TV and TV-L1 regularization.

Dependencies:
- torch, torch.nn, torch.optim
- numpy
- matplotlib
- pytorch_lightning
- proxtorch

.. GENERATED FROM PYTHON SOURCE LINES 16-127



.. image-sg:: /auto_examples/images/sphx_glr_plot_image_restoration_001.png
   :alt: Original, Noisy, TV Restored, TV-L1 Restored
   :srcset: /auto_examples/images/sphx_glr_plot_image_restoration_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/proxtorch/envs/latest/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
      warning_cache.warn(
    /home/docs/checkouts/readthedocs.org/user_builds/proxtorch/envs/latest/lib/python3.11/site-packages/pytorch_lightning/loops/fit_loop.py:281: PossibleUserWarning: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
      rank_zero_warn(

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    Epoch 49: 100%|██████████| 1/1 [00:00<00:00,  3.01it/s, v_num=1]
    TV loss: 0.011123144324086474
    TV-L1 loss: 0.011258230608062036






|

.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np
    import pytorch_lightning as pl
    import torch
    import torch.optim as optim
    from pytorch_lightning import seed_everything
    from torch.utils.data import DataLoader, TensorDataset

    from proxtorch.operators import TVL1_2D

    # Set seed
    seed_everything(42)

    # Load ProxTorch Logo as jpg then convert to grayscale numpy array
    proxtorch_logo = plt.imread("../proxtorch-logo.jpg")
    # Downsample to 64x64
    proxtorch_logo = proxtorch_logo[::4, ::4]
    proxtorch_logo = 1 - np.mean(proxtorch_logo, axis=2)
    # Normalize to [0, 1]
    proxtorch_logo = (proxtorch_logo - np.min(proxtorch_logo)) / (
        np.max(proxtorch_logo) - np.min(proxtorch_logo)
    )


    class TVL1Restoration(pl.LightningModule):
        def __init__(self, alpha, l1_ratio):
            super().__init__()
            self.restored = torch.nn.Parameter(torch.zeros(proxtorch_logo.shape))
            self.tvl1_prox = TVL1_2D(alpha=alpha, l1_ratio=l1_ratio)
            self.automatic_optimization = False

        def forward(self, x):
            return self.restored

        def training_step(self, batch, _):
            opt = self.optimizers()
            opt.zero_grad()
            noisy, original = batch
            y_hat = self.restored
            loss = torch.sum((y_hat - noisy) ** 2)
            tv_loss = self.tvl1_prox(self.restored)
            self.manual_backward(loss)
            opt.step()
            with torch.no_grad():
                optimizer = self.trainer.optimizers[0]
                self.restored.data = self.tvl1_prox.prox(
                    self.restored.data, optimizer.param_groups[0]["lr"]
                )

        def configure_optimizers(self):
            return optim.SGD(self.parameters(), lr=0.01)


    # Data Preparation
    noisy_logo = proxtorch_logo + np.random.normal(
        loc=0, scale=0.1, size=proxtorch_logo.shape
    )
    dataset = TensorDataset(
        torch.tensor(noisy_logo).unsqueeze(0), torch.tensor(proxtorch_logo).unsqueeze(0)
    )
    loader = DataLoader(dataset, batch_size=1)

    # Model Initialization
    tv_l1_model = TVL1Restoration(alpha=0.2, l1_ratio=0.05)
    tv_model = TVL1Restoration(alpha=0.2, l1_ratio=0.0)

    # Training
    trainer = pl.Trainer(max_epochs=50)
    trainer.fit(tv_model, loader)
    trainer = pl.Trainer(max_epochs=50)
    trainer.fit(tv_l1_model, loader)


    # Evaluation
    def evaluate(model, label):
        model.eval()
        loss = torch.mean((model.restored - torch.tensor(proxtorch_logo).unsqueeze(0)) ** 2)
        print(f"{label} loss: {loss.item()}")


    evaluate(tv_model, "TV")
    evaluate(tv_l1_model, "TV-L1")

    # Determine the global min and max across all images to set a consistent colorscale
    global_min = 0
    global_max = 1

    # Visualization
    fig, ax = plt.subplots(1, 4, figsize=(20, 5))

    # Original Image
    ax[0].imshow(proxtorch_logo, cmap="gray", vmin=global_min, vmax=global_max)
    ax[0].set_title("Original")

    # Noisy Image
    ax[1].imshow(noisy_logo, cmap="gray", vmin=global_min, vmax=global_max)
    ax[1].set_title("Noisy")

    # TV Restored Image
    tv_restored_image = tv_model.restored.detach().numpy()
    ax[2].imshow(tv_restored_image, cmap="gray", vmin=global_min, vmax=global_max)
    ax[2].set_title("TV Restored")

    # TV-L1 Restored Image
    tv_l1_restored_image = tv_l1_model.restored.detach().numpy()
    ax[3].imshow(tv_l1_restored_image, cmap="gray", vmin=global_min, vmax=global_max)
    ax[3].set_title("TV-L1 Restored")

    plt.tight_layout()
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 33.501 seconds)


.. _sphx_glr_download_auto_examples_plot_image_restoration.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_image_restoration.py <plot_image_restoration.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_image_restoration.ipynb <plot_image_restoration.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_