.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plug-and-play/demo_PnP_DPIR_deblur.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_plug-and-play_demo_PnP_DPIR_deblur.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plug-and-play_demo_PnP_DPIR_deblur.py:


DPIR method for PnP image deblurring.
====================================================================================================

This example shows how to use the DPIR method to solve a PnP image deblurring problem. The DPIR method is described in
the following paper:
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017).
Learning deep CNN denoiser prior for image restoration.
In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3929-3938).

.. GENERATED FROM PYTHON SOURCE LINES 11-25

.. code-block:: Python


    import deepinv as dinv
    from pathlib import Path
    import torch
    from torch.utils.data import DataLoader
    from deepinv.models import DRUNet
    from deepinv.optim.data_fidelity import L2
    from deepinv.optim.prior import PnP
    from deepinv.optim.optimizers import optim_builder
    from deepinv.training import test
    from torchvision import transforms
    from deepinv.optim.dpir import get_DPIR_params
    from deepinv.utils.demo import load_dataset, load_degradation








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Setup paths for data loading and results.
----------------------------------------------------------------------------------------


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.. code-block:: Python


    BASE_DIR = Path(".")
    DATA_DIR = BASE_DIR / "measurements"
    RESULTS_DIR = BASE_DIR / "results"
    DEG_DIR = BASE_DIR / "degradations"









.. GENERATED FROM PYTHON SOURCE LINES 37-42

Load base image datasets and degradation operators.
----------------------------------------------------------------------------------------
In this example, we use the Set3C dataset and a motion blur kernel from
`Levin et al. (2009) <https://ieeexplore.ieee.org/abstract/document/5206815/>`_.


.. GENERATED FROM PYTHON SOURCE LINES 42-65

.. code-block:: Python


    # Set the global random seed from pytorch to ensure reproducibility of the example.
    torch.manual_seed(0)

    device = dinv.utils.get_freer_gpu() if torch.cuda.is_available() else "cpu"

    # Set up the variable to fetch dataset and operators.
    method = "DPIR"
    dataset_name = "set3c"
    img_size = 128 if torch.cuda.is_available() else 32
    val_transform = transforms.Compose(
        [transforms.CenterCrop(img_size), transforms.ToTensor()]
    )

    # Generate a motion blur operator.
    kernel_index = 1  # which kernel to chose among the 8 motion kernels from 'Levin09.mat'
    kernel_torch = load_degradation("Levin09.npy", DEG_DIR / "kernels", index=kernel_index)
    kernel_torch = kernel_torch.unsqueeze(0).unsqueeze(
        0
    )  # add batch and channel dimensions
    dataset = load_dataset(dataset_name, transform=val_transform)






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

 .. code-block:: none

    Levin09.npy degradation downloaded in degradations/kernels
    Downloading datasets/set3c.zip

      0%|          | 0.00/385k [00:00<?, ?iB/s]
    100%|██████████| 385k/385k [00:00<00:00, 59.8MiB/s]
    set3c dataset downloaded in datasets




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Generate a dataset of blurred images and load it.
--------------------------------------------------------------------------------
We use the BlurFFT class from the physics module to generate a dataset of blurred images.

.. GENERATED FROM PYTHON SOURCE LINES 69-102

.. code-block:: Python



    noise_level_img = 0.03  # Gaussian Noise standard deviation for the degradation
    n_channels = 3  # 3 for color images, 1 for gray-scale images
    p = dinv.physics.BlurFFT(
        img_size=(n_channels, img_size, img_size),
        filter=kernel_torch,
        device=device,
        noise_model=dinv.physics.GaussianNoise(sigma=noise_level_img),
    )

    # Use parallel dataloader if using a GPU to fasten training,
    # otherwise, as all computes are on CPU, use synchronous data loading.
    num_workers = 4 if torch.cuda.is_available() else 0

    n_images_max = 3  # Maximal number of images to restore from the input dataset
    # Generate a dataset in a HDF5 folder in "{dir}/dinv_dataset0.h5'" and load it.
    operation = "deblur"
    measurement_dir = DATA_DIR / dataset_name / operation
    dinv_dataset_path = dinv.datasets.generate_dataset(
        train_dataset=dataset,
        test_dataset=None,
        physics=p,
        device=device,
        save_dir=measurement_dir,
        train_datapoints=n_images_max,
        num_workers=num_workers,
    )

    batch_size = 3  # batch size for testing. As the number of iterations is fixed, we can use batch_size > 1
    # and restore multiple images in parallel.
    dataset = dinv.datasets.HDF5Dataset(path=dinv_dataset_path, train=True)





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

 .. code-block:: none

    Dataset has been saved at measurements/set3c/deblur/dinv_dataset0.h5




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Set up the DPIR algorithm to solve the inverse problem.
--------------------------------------------------------------------------------
This method is based on half-quadratic splitting (HQS).
The algorithm alternates between a denoising step and a data fidelity step, where
the denoising step is performed by a pretrained denoiser :class:`deepinv.models.DRUNet`.

.. note::
   We provide a wrapper for rapidly creating the DPIR algorithm in :class:`deepinv.optim.DPIR`.

.. GENERATED FROM PYTHON SOURCE LINES 111-138

.. code-block:: Python


    # load specific parameters for DPIR
    sigma_denoiser, stepsize, max_iter = get_DPIR_params(noise_level_img)
    params_algo = {"stepsize": stepsize, "g_param": sigma_denoiser}
    early_stop = False  # Do not stop algorithm with convergence criteria

    # Select the data fidelity term
    data_fidelity = L2()

    # Specify the denoising prior
    prior = PnP(denoiser=DRUNet(pretrained="download", device=device))

    # instantiate the algorithm class to solve the IP problem.
    model = optim_builder(
        iteration="HQS",
        prior=prior,
        data_fidelity=data_fidelity,
        early_stop=early_stop,
        max_iter=max_iter,
        verbose=True,
        params_algo=params_algo,
    )

    # Set the model to evaluation mode. We do not require training here.
    model.eval()






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

 .. code-block:: none


    BaseOptim(
      (fixed_point): FixedPoint(
        (iterator): HQSIteration(
          (f_step): fStepHQS()
          (g_step): gStepHQS()
        )
      )
    )



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Evaluate the model on the problem.
--------------------------------------------------------------------
The test function evaluates the model on the test dataset and computes the metrics.


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.. code-block:: Python


    save_folder = RESULTS_DIR / method / operation / dataset_name
    plot_convergence_metrics = True  # Metrics are saved in save_folder.
    plot_images = True  # Images are saved in save_folder.

    dataloader = DataLoader(
        dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False
    )

    test(
        model=model,
        test_dataloader=dataloader,
        physics=p,
        metrics=[dinv.metric.PSNR(), dinv.metric.LPIPS(device=device)],
        device=device,
        plot_images=plot_images,
        save_folder=save_folder,
        plot_convergence_metrics=plot_convergence_metrics,
        verbose=True,
    )



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/plug-and-play/images/sphx_glr_demo_PnP_DPIR_deblur_001.png
         :alt: Ground truth, Measurement, No learning, Reconstruction
         :srcset: /auto_examples/plug-and-play/images/sphx_glr_demo_PnP_DPIR_deblur_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/plug-and-play/images/sphx_glr_demo_PnP_DPIR_deblur_002.png
         :alt: PSNR, residual
         :srcset: /auto_examples/plug-and-play/images/sphx_glr_demo_PnP_DPIR_deblur_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    Loading pretrained model LPIPS from /home/runner/.cache/torch/hub/pyiqa/LPIPS_v0.1_alex-df73285e.pth
    2025-03-13 09:16:02,160 INFO: Network [LPIPS] is created.
    2025-03-13 09:16:02,160 INFO: Metric [LPIPS] is created.

      0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]
    Test:   0%|                                                                                                                     | 0/1 [00:00<?, ?it/s]
    Test:   0%|                                           | 0/1 [00:01<?, ?it/s, PSNR=30.2, PSNR no learning=4.19, LPIPS=0.00378, LPIPS no learning=0.402]
    Test: 100%|███████████████████████████████████| 1/1 [00:01<00:00,  1.87s/it, PSNR=30.2, PSNR no learning=4.19, LPIPS=0.00378, LPIPS no learning=0.402]
    Test: 100%|███████████████████████████████████| 1/1 [00:01<00:00,  1.87s/it, PSNR=30.2, PSNR no learning=4.19, LPIPS=0.00378, LPIPS no learning=0.402]
    Test results:
    PSNR no learning: 4.187 +- 1.447
    PSNR: 30.165 +- 2.016
    LPIPS no learning: 0.402 +- 0.134
    LPIPS: 0.004 +- 0.003

    {'PSNR no learning': np.float64(4.186646143595378), 'PSNR no learning_std': np.float64(1.4468237274226996), 'PSNR': np.float64(30.16461435953776), 'PSNR_std': np.float64(2.0164958180264647), 'LPIPS no learning': np.float64(0.4024091561635335), 'LPIPS no learning_std': np.float64(0.1336401602927466), 'LPIPS': np.float64(0.0037754916896422706), 'LPIPS_std': np.float64(0.002592722410522517)}




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

   **Total running time of the script:** (0 minutes 2.872 seconds)


.. _sphx_glr_download_auto_examples_plug-and-play_demo_PnP_DPIR_deblur.py:

.. only:: html

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

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

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

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

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

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      :download:`Download zipped: demo_PnP_DPIR_deblur.zip <demo_PnP_DPIR_deblur.zip>`


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