# ArtifactRemoval

### *class* deepinv.models.ArtifactRemoval(backbone_net, mode='adjoint', ckpt_path=None, device=None)

Bases: [`Reconstructor`](https://deepinv.org/api/stubs/deepinv.models.Reconstructor.html.md#deepinv.models.Reconstructor)

Artifact removal architecture.

Transforms a denoiser $\phi$ into a reconstruction network $R$ by doing

- Adjoint: $\inversef{y}{A}=\phi(A^{\top}y)$ with `mode='adjoint'`.
- Pseudoinverse: $\inversef{y}{A}=\phi(A^{\dagger}y)$ with `mode='pinv'`.
- Direct: $\inversef{y}{A}=\phi(y)$ with `mode='direct'`.

#### NOTE
In the case of `mode='pinv'`, the architecture is inspired by the FBPConvNet
approach Jin *et al.*<sup>[1](#footcite-jin2017deep)</sup> where a deep network $\phi$
is used to improve the filtered back projection $A^{\dagger}y$.

* **Parameters:**
  * **backbone_net** ([*deepinv.models.Denoiser*](https://deepinv.org/api/stubs/deepinv.models.Denoiser.html.md#deepinv.models.Denoiser) *,* [*torch.nn.Module*](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module)) – Base denoiser network $\phi$
    (see [Denoisers](https://deepinv.org/user_guide/reconstruction/denoisers.html.md#denoisers) for available architectures).
  * **mode** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – Reconstruction mode. Options are ‘direct’, ‘adjoint’ or ‘pinv’.
  * **device** ([*torch.device*](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device)) – cpu or gpu.

<hr />

* **References:**

* <a id='footcite-jin2017deep'>**[1]**</a> Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, and Michael Unser. Deep convolutional neural network for inverse problems in imaging. *IEEE transactions on image processing*, 26(9):4509–4522, 2017.

#### backbone_inference(tensor_in, physics, y, \*\*kwargs)

Perform inference on the backbone network.

By default, treats backbone network as a denoiser.
Override for different inference e.g. for an unrolled network.

* **Parameters:**
  * **tensor_in** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input tensor as dictated by ArtifactRemoval mode
  * **physics** ([*Physics*](https://deepinv.org/api/stubs/deepinv.physics.Physics.html.md#deepinv.physics.Physics)) – forward physics
  * **y** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input measurements y
* **Returns:**
  reconstructed image
* **Return type:**
  [torch.Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)

#### forward(y, physics, \*\*kwargs)

Reconstructs a signal estimate from measurements y

* **Parameters:**
  * **y** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – measurements
  * **physics** ([*deepinv.physics.Physics*](https://deepinv.org/api/stubs/deepinv.physics.Physics.html.md#deepinv.physics.Physics)) – forward operator
  * **kwargs** ([*dict*](https://docs.python.org/3.12/library/stdtypes.html#dict)) – additional keyword arguments for the backbone network.
* **Returns:**
  reconstructed image
* **Return type:**
  [torch.Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)

<a id="sphx-glr-backref-deepinv-models-artifactremoval"></a>

## Examples using `ArtifactRemoval`:

<div class="sphx-glr-thumbnails">
<!-- thumbnail-parent-div-open --><div class="sphx-glr-thumbcontainer" tooltip="This example provides a very simple quick start introduction to training reconstruction networks with DeepInverse for solving imaging inverse problems.">![](auto_examples/models/images/thumb/sphx_glr_demo_training_thumb.png)

[Training a reconstruction model](https://deepinv.org/auto_examples/models/demo_training.html.md)

  <div class="sphx-glr-thumbnail-title">Training a reconstruction model</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example presents the various datasets, forward physics and models available in DeepInverse for Magnetic Resonance Imaging (MRI) problems:">![](auto_examples/physics/images/thumb/sphx_glr_demo_mri_tour_thumb.png)

[Tour of MRI functionality in DeepInverse](https://deepinv.org/auto_examples/physics/demo_mri_tour.html.md)

  <div class="sphx-glr-thumbnail-title">Tour of MRI functionality in DeepInverse</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows you how to train a reconstruction network for an inpainting inverse problem on a fully self-supervised way, i.e., using measurement data only.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_multioperator_imaging_thumb.png)

[Self-supervised learning from incomplete measurements of multiple operators.](https://deepinv.org/auto_examples/self-supervised-learning/demo_multioperator_imaging.html.md)

  <div class="sphx-glr-thumbnail-title">Self-supervised learning from incomplete measurements of multiple operators.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows you how to train a denoiser network in a fully self-supervised way, i.e., using noisy images only via the Neighbor2Neighbor loss, which exploits the local correlation of natural images.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_n2n_denoising_thumb.png)

[Self-supervised denoising with the Neighbor2Neighbor loss.](https://deepinv.org/auto_examples/self-supervised-learning/demo_n2n_denoising.html.md)

  <div class="sphx-glr-thumbnail-title">Self-supervised denoising with the Neighbor2Neighbor loss.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows you how to train a denoiser network in a fully self-supervised way, using noisy images only via the Generalized Recorrupted2Recorrupted (GR2R) loss monroy2025generalized, which exploits knowledge about the noise distribution. You can change the noise distribution by selecting from predefined noise models such as Gaussian, Poisson, and Gamma noise.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_r2r_denoising_thumb.png)

[Self-supervised denoising with the Generalized R2R loss.](https://deepinv.org/auto_examples/self-supervised-learning/demo_r2r_denoising.html.md)

  <div class="sphx-glr-thumbnail-title">Self-supervised denoising with the Generalized R2R loss.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We demonstrate self-supervised learning with measurement splitting, to train a denoiser network on the MNIST dataset. The physics here is noisy computed tomography, as is the case in Noise2Inverse hendriksen2020noise2inverse. Note this example can also be easily applied to undersampled multicoil MRI as is the case in SSDU yaman2020self.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_splitting_loss_thumb.png)

[Self-supervised learning with measurement splitting](https://deepinv.org/auto_examples/self-supervised-learning/demo_splitting_loss.html.md)

  <div class="sphx-glr-thumbnail-title">Self-supervised learning with measurement splitting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows you how to train a denoiser network in a fully self-supervised way, i.e., using noisy images only via the SURE loss, which exploits knowledge about the noise distribution.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_sure_denoising_thumb.png)

[Self-supervised denoising with the SURE loss.](https://deepinv.org/auto_examples/self-supervised-learning/demo_sure_denoising.html.md)

  <div class="sphx-glr-thumbnail-title">Self-supervised denoising with the SURE loss.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows you how to train a denoiser network in a fully self-supervised way, i.e., using noisy images with unknown noise level only via the UNSURE loss, which is introduced by tachella2024unsure.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_unsure_thumb.png)

[Self-supervised denoising with the UNSURE loss.](https://deepinv.org/auto_examples/self-supervised-learning/demo_unsure.html.md)

  <div class="sphx-glr-thumbnail-title">Self-supervised denoising with the UNSURE loss.</div>
</div>
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