# load_example

### deepinv.utils.load_example(name, img_size=None, grayscale=False, resize_mode='crop', device='cpu', dtype=torch.float32)

Load example image from the [DeepInverse HuggingFace](https://huggingface.co/datasets/deepinv/images).

Uses [`deepinv.utils.load_url_image()`](https://deepinv.org/api/stubs/deepinv.utils.load_url_image.html.md#deepinv.utils.load_url_image) if image file or [`deepinv.utils.load_torch_url()`](https://deepinv.org/api/stubs/deepinv.utils.load_torch_url.html.md#deepinv.utils.load_torch_url) if torch tensor in `.pt` file
or [`deepinv.utils.load_np_url()`](https://deepinv.org/api/stubs/deepinv.utils.load_np_url.html.md#deepinv.utils.load_np_url) if numpy array in `npy` or `npz` file.

Also adds a batch dimension to the image.

Available examples for `name` include (see [the HuggingFace repo](https://huggingface.co/datasets/deepinv/images) for full list):

#### Example Images

| Name                                                           | Origin                                                                                                | Image size                     | Domain     |
|----------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|--------------------------------|------------|
| `barbara.jpeg`, `butterfly.png`                                | [`Set14`](https://deepinv.org/api/stubs/deepinv.datasets.Set14HR.html.md#deepinv.datasets.Set14HR)                       | (3, 512, 512), (3, 256, 256)   | natural    |
| `cameraman.png`                                                | Classic toy image                                                                                     | (1, 512, 512)                  | natural    |
| `CBSD_0010.png`                                                | [`CBSD68`](https://deepinv.org/api/stubs/deepinv.datasets.CBSD68.html.md#deepinv.datasets.CBSD68)                       | (2, 481, 321)                  | natural    |
| `celeba_example.jpg`                                           | CelebA                                                                                                | (3, 1024, 1024)                | natural    |
| `div2k_valid_hr_0877.png`, `div2k_valid_lr_bicubic_0877x4.png` | GT and measurement from [`Div2k`](https://deepinv.org/api/stubs/deepinv.datasets.DIV2K.html.md#deepinv.datasets.DIV2K) | (3, 1152, 2040), (3, 288, 510) | natural    |
| `leaves.png`                                                   | Set3C dataset                                                                                         | (3, 256, 256)                  | natural    |
| `mbappe.jpg`                                                   |                                                                                                       | (3, 443, 664)                  | natural    |
| `CT100_256x256_0.pt`                                           | [CT100](https://doi.org/10.1007/s10278-013-9622-7)                                                    | (1, 256, 256)                  | medical    |
| `brainweb_t1_ICBM_1mm_subject_0.npy`                           | [BrainWeb](https://brainweb.bic.mni.mcgill.ca/brainweb/) 3D MRI data                                  | (181, 217, 181)                | medical    |
| `demo_mini_subset_fastmri_brain_0.pt`                          | [`FastMRI`](https://deepinv.org/api/stubs/deepinv.datasets.SimpleFastMRISliceDataset.html.md#deepinv.datasets.SimpleFastMRISliceDataset)   | (2, 320, 320)                  | medical    |
| `SheppLogan.png`                                               | Shepp Logan phantom                                                                                   | (4, 512, 512)                  | medical    |
| `FMD_TwoPhoton_MICE_R_gt_12_avg50.png`                         | [`FMD`](https://deepinv.org/api/stubs/deepinv.datasets.FMD.html.md#deepinv.datasets.FMD)                             | (3, 512, 512)                  | microscopy |
| `JAX_018_011_RGB.tif`                                          | Sample RGB patch from WorldView-3                                                                     | (3, 1024, 1024)                | satellite  |
* **Parameters:**
  * **name** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – filename of the image from the HuggingFace dataset.
  * **img_size** ([*int*](https://docs.python.org/3.12/library/functions.html#int) *,* [*tuple*](https://docs.python.org/3.12/library/stdtypes.html#tuple) *[*[*int*](https://docs.python.org/3.12/library/functions.html#int) *]*) – Size of the image to return.
  * **grayscale** ([*bool*](https://docs.python.org/3.12/library/functions.html#bool)) – Whether to convert the image to grayscale.
  * **resize_mode** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – If `img_size` is not None, options are `"crop"` or `"resize"`.
  * **device** ([*torch.device*](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device) *,* [*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – Device on which to load the image (gpu or cpu).
  * **dtype** ([*torch.dtype*](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)) – torch dtype to cast the image to.
* **Returns:**
  [`torch.Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor) containing the image with an added batch dimension.

## Examples using `load_example`:

<div class="sphx-glr-thumbnails">
<!-- thumbnail-parent-div-open --><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use DeepInverse with your own dataset.">![](auto_examples/basics/images/thumb/sphx_glr_demo_custom_dataset_thumb.png)

[Bring your own dataset](https://deepinv.org/auto_examples/basics/demo_custom_dataset.html.md)

  <div class="sphx-glr-thumbnail-title">Bring your own dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Follow this example to reconstruct images using an iterative algorithm.">![](auto_examples/basics/images/thumb/sphx_glr_demo_custom_optim_thumb.png)

[Use iterative reconstruction algorithms](https://deepinv.org/auto_examples/basics/demo_custom_optim.html.md)

  <div class="sphx-glr-thumbnail-title">Use iterative reconstruction algorithms</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Follow this example to reconstruct images using a pretrained model in one line.">![](auto_examples/basics/images/thumb/sphx_glr_demo_pretrained_model_thumb.png)

[Use a pretrained model](https://deepinv.org/auto_examples/basics/demo_pretrained_model.html.md)

  <div class="sphx-glr-thumbnail-title">Use a pretrained model</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Follow this example to get started with DeepInverse in under 5 minutes.">![](auto_examples/basics/images/thumb/sphx_glr_demo_quickstart_thumb.png)

[5 minute quickstart tutorial](https://deepinv.org/auto_examples/basics/demo_quickstart.html.md)

  <div class="sphx-glr-thumbnail-title">5 minute quickstart tutorial</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates blind image deblurring using the pretrained kernel estimation network from the paper carbajal2023blind. The network estimates spatially-varying blur kernels from a blurred image, which are then used in a space-varying blur physics model to reconstruct the sharp image using a non-blind deblurring algorithm.">![](auto_examples/blind-inverse-problems/images/thumb/sphx_glr_demo_blind_deblurring_thumb.png)

[Blind deblurring with kernel estimation network](https://deepinv.org/auto_examples/blind-inverse-problems/demo_blind_deblurring.html.md)

  <div class="sphx-glr-thumbnail-title">Blind deblurring with kernel estimation network</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example focuses on blind image Gaussian denoising, i.e. the problem">![](auto_examples/blind-inverse-problems/images/thumb/sphx_glr_demo_blind_denoising_thumb.png)

[Blind denoising with noise level estimation](https://deepinv.org/auto_examples/blind-inverse-problems/demo_blind_denoising.html.md)

  <div class="sphx-glr-thumbnail-title">Blind denoising with noise level estimation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example introduces the Richardson-Lucy algorithm for deconvolution in the blind setting, where both the underlying clean image and the blur kernel are unknown.">![](auto_examples/blind-inverse-problems/images/thumb/sphx_glr_demo_blind_richardsonlucy_thumb.png)

[Blind Richardson-Lucy deconvolution](https://deepinv.org/auto_examples/blind-inverse-problems/demo_blind_richardsonlucy.html.md)

  <div class="sphx-glr-thumbnail-title">Blind Richardson-Lucy deconvolution</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In many imaging problems, the data to be processed can be very large, making it challenging to fit the denoising process into the memory of a single device. For instance, medical imaging or satellite imagery often involves processing gigapixel images that cannot be processed as a whole.">![](auto_examples/distributed/images/thumb/sphx_glr_demo_denoiser_distributed_thumb.png)

[Distributed Denoiser with Image Tiling](https://deepinv.org/auto_examples/distributed/demo_denoiser_distributed.html.md)

  <div class="sphx-glr-thumbnail-title">Distributed Denoiser with Image Tiling</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Many large-scale imaging problems involve operators that can be naturally decomposed as a stack of multiple sub-operators:">![](auto_examples/distributed/images/thumb/sphx_glr_demo_physics_distributed_thumb.png)

[Distributed Physics Operators](https://deepinv.org/auto_examples/distributed/demo_physics_distributed.html.md)

  <div class="sphx-glr-thumbnail-title">Distributed Physics Operators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Many large-scale imaging problems involve operators that can be naturally decomposed as a stack of multiple sub-operators:">![](auto_examples/distributed/images/thumb/sphx_glr_demo_pnp_distributed_thumb.png)

[Distributed Plug-and-Play (PnP) Reconstruction](https://deepinv.org/auto_examples/distributed/demo_pnp_distributed.html.md)

  <div class="sphx-glr-thumbnail-title">Distributed Plug-and-Play (PnP) Reconstruction</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use Spyrit linear models and measurements with DeepInverse. Here we use the HadamSplit2d linear model from Spyrit.">![](auto_examples/external-libraries/images/thumb/sphx_glr_demo_connect_spyrit_thumb.png)

[Single-pixel imaging with Spyrit](https://deepinv.org/auto_examples/external-libraries/demo_connect_spyrit.html.md)

  <div class="sphx-glr-thumbnail-title">Single-pixel imaging with Spyrit</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In blind inverse problems, some parameters of the physics are unknown at test time. Running non-blind models requires knowing these parameters, which are often hard to estimate. For example, a denoiser needs the noise level \\sigma, a deblurring model needs the blur kernel.">![](auto_examples/metrics/images/thumb/sphx_glr_demo_test_time_tuning_thumb.png)

[Blind inverse problems with no reference metrics](https://deepinv.org/auto_examples/metrics/demo_test_time_tuning.html.md)

  <div class="sphx-glr-thumbnail-title">Blind inverse problems with no reference metrics</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example provides a tour of the denoisers in DeepInverse. A denoiser is a model that takes in a noisy image and outputs a denoised version of it. Basically, it solves the following problem:">![](auto_examples/models/images/thumb/sphx_glr_demo_denoiser_tour_thumb.png)

[Benchmarking pretrained denoisers](https://deepinv.org/auto_examples/models/demo_denoiser_tour.html.md)

  <div class="sphx-glr-thumbnail-title">Benchmarking pretrained denoisers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform inference on and fine-tune the Reconstruct Anything Model (RAM) foundation model terris2025reconstruct to solve inverse problems.">![](auto_examples/models/images/thumb/sphx_glr_demo_foundation_model_thumb.png)

[Inference and fine-tune a foundation model](https://deepinv.org/auto_examples/models/demo_foundation_model.html.md)

  <div class="sphx-glr-thumbnail-title">Inference and fine-tune a foundation model</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Single-image super-resolution (SISR) is the inverse problem of recovering a high-resolution (HR) image x from a low-resolution (LR) observation y = \\downarrow_s(x), where \\downarrow_s denotes downsampling by factor s.">![](auto_examples/models/images/thumb/sphx_glr_demo_super_resolution_thumb.png)

[Super-resolution with SRResNet](https://deepinv.org/auto_examples/models/demo_super_resolution.html.md)

  <div class="sphx-glr-thumbnail-title">Super-resolution with SRResNet</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This code shows how to reconstruct a noisy and incomplete image using the deep image prior.">![](auto_examples/optimization/images/thumb/sphx_glr_demo_dip_thumb.png)

[Reconstructing an image using the deep image prior.](https://deepinv.org/auto_examples/optimization/demo_dip.html.md)

  <div class="sphx-glr-thumbnail-title">Reconstructing an image using the deep image prior.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we use the expected patch log likelihood (EPLL) prior zoran2011learning. for denoising and inpainting of natural images. To this end, we consider the inverse problem y = Ax+\\epsilon, where A is either the identity (for denoising) or a masking operator (for inpainting) and \\epsilon\\sim\\mathcal{N}(0,\\sigma^2 I) is white Gaussian noise with standard deviation \\sigma.">![](auto_examples/optimization/images/thumb/sphx_glr_demo_epll_thumb.png)

[Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting](https://deepinv.org/auto_examples/optimization/demo_epll.html.md)

  <div class="sphx-glr-thumbnail-title">Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to solve Poisson inverse problems using the Maximum-Likelihood Expectation-Maximization (MLEM) algorithm sheppMaximumLikelihoodReconstruction1982, also known as the Richardson-Lucy algorithm in the deconvolution setting richardsonBayesianBasedIterativeMethod1972,lucyIterativeTechniqueRectification1974.">![](auto_examples/optimization/images/thumb/sphx_glr_demo_poisson_mlem_thumb.png)

[Poisson Inverse Problems with Maximum-Likelihood Expectation-Maximization (MLEM)](https://deepinv.org/auto_examples/optimization/demo_poisson_mlem.html.md)

  <div class="sphx-glr-thumbnail-title">Poisson Inverse Problems with Maximum-Likelihood Expectation-Maximization (MLEM)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to denoise images corrupted by Poisson-Gaussian noise using the Generalized Anscombe Transform (GAT), which converts any Gaussian denoiser into a Poisson-Gaussian denoiser makitalo2012optimal.">![](auto_examples/physics/images/thumb/sphx_glr_demo_anscombe_thumb.png)

[Poisson-Gaussian Denoising with the Generalized Anscombe Transform](https://deepinv.org/auto_examples/physics/demo_anscombe.html.md)

  <div class="sphx-glr-thumbnail-title">Poisson-Gaussian Denoising with the Generalized Anscombe Transform</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example provides a tour of 2D blur operators in DeepInverse. In particular, we show how to use DiffractionBlurs (Fresnel diffraction), motion blurs and space varying blurs.">![](auto_examples/physics/images/thumb/sphx_glr_demo_blur_tour_thumb.png)

[Tour of blur operators](https://deepinv.org/auto_examples/physics/demo_blur_tour.html.md)

  <div class="sphx-glr-thumbnail-title">Tour of blur operators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Real-world blurry images have decorrelated opposite boundaries, unlike images synthetically blurred using circular filters. This makes the use of spectral deconvolution methods (inverse filtering, Wiener filtering) impractical and prone to ringing artifacts. Liu-Jia padding liu2008reducing is a pre-processing step that pads the input image to make it have smooth circular boundaries, while preserving the original spectral content as much as possible.">![](auto_examples/physics/images/thumb/sphx_glr_demo_liu_jia_padding_thumb.png)

[Spectral Methods for Non-Circular Deblurring with Liu-Jia Padding](https://deepinv.org/auto_examples/physics/demo_liu_jia_padding.html.md)

  <div class="sphx-glr-thumbnail-title">Spectral Methods for Non-Circular Deblurring with Liu-Jia Padding</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a random phase retrieval operator and generate phaseless measurements from a given image. The example showcases 4 different reconstruction methods to recover the image from the phaseless measurements:">![](auto_examples/physics/images/thumb/sphx_glr_demo_phase_retrieval_thumb.png)

[Random phase retrieval and reconstruction methods.](https://deepinv.org/auto_examples/physics/demo_phase_retrieval.html.md)

  <div class="sphx-glr-thumbnail-title">Random phase retrieval and reconstruction methods.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example provides a tour of some of the forward operators implemented in DeepInverse. We restrict ourselves to operators where the signal is a 2D image. The full list of operators can be found in here.">![](auto_examples/physics/images/thumb/sphx_glr_demo_physics_tour_thumb.png)

[Tour of forward sensing operators](https://deepinv.org/auto_examples/physics/demo_physics_tour.html.md)

  <div class="sphx-glr-thumbnail-title">Tour of forward sensing operators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a Ptychography phase retrieval operator and generate phaseless measurements from a given image.">![](auto_examples/physics/images/thumb/sphx_glr_demo_ptychography_thumb.png)

[Ptychography phase retrieval](https://deepinv.org/auto_examples/physics/demo_ptychography.html.md)

  <div class="sphx-glr-thumbnail-title">Ptychography phase retrieval</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we show how to use the deepinv.physics.Scattering forward model.">![](auto_examples/physics/images/thumb/sphx_glr_demo_scattering_thumb.png)

[Inverse scattering problem](https://deepinv.org/auto_examples/physics/demo_scattering.html.md)

  <div class="sphx-glr-thumbnail-title">Inverse scattering problem</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This demo shows the use of the deepinv.physics.SpatialUnwrapping forward model and the deepinv.optim.ItohFidelity for unwrapping problems, which occur in modulo imaging, interferometry SAR and other imaging applications. It shows how to generate a wrapped phase image, apply blur and noise, and reconstruct the original phase using both DCT inversion and ADMM optimization.">![](auto_examples/physics/images/thumb/sphx_glr_demo_spatial_unwrapping_thumb.png)

[Spatial unwrapping and modulo imaging](https://deepinv.org/auto_examples/physics/demo_spatial_unwrapping.html.md)

  <div class="sphx-glr-thumbnail-title">Spatial unwrapping and modulo imaging</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to define your own optimization algorithm. For example, here, we implement the Primal-Dual Condat-Vu (CV) algorithm, and apply it for Single Pixel Camera reconstruction.">![](auto_examples/plug-and-play/images/thumb/sphx_glr_demo_PnP_custom_optim_thumb.png)

[PnP with custom optimization algorithm (Primal-Dual Condat-Vu)](https://deepinv.org/auto_examples/plug-and-play/demo_PnP_custom_optim.html.md)

  <div class="sphx-glr-thumbnail-title">PnP with custom optimization algorithm (Primal-Dual Condat-Vu)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is a simple example to show how to use a mirror descent algorithm for solving an inverse problem with Poisson noise. See hurault2023convergent for more details.">![](auto_examples/plug-and-play/images/thumb/sphx_glr_demo_PnP_mirror_descent_thumb.png)

[Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.](https://deepinv.org/auto_examples/plug-and-play/demo_PnP_mirror_descent.html.md)

  <div class="sphx-glr-thumbnail-title">Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use a standard PnP algorithm with DnCNN denoiser for computed tomography.">![](auto_examples/plug-and-play/images/thumb/sphx_glr_demo_vanilla_PnP_thumb.png)

[Vanilla PnP for computed tomography (CT).](https://deepinv.org/auto_examples/plug-and-play/demo_vanilla_PnP.html.md)

  <div class="sphx-glr-thumbnail-title">Vanilla PnP for computed tomography (CT).</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This code shows how to build your custom sampling kernel. Here we build a preconditioned Unadjusted Langevin Algorithm (PreconULA) that takes advantage of the singular value decomposition of the forward operator to accelerate the sampling.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_custom_kernel_thumb.png)

[Building your custom MCMC sampling algorithm.](https://deepinv.org/auto_examples/sampling/demo_custom_kernel.html.md)

  <div class="sphx-glr-thumbnail-title">Building your custom MCMC sampling algorithm.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This code shows you how to use the DDRM diffusion algorithm kawar2022denoising to reconstruct images and also compute the uncertainty of a reconstruction from incomplete and noisy measurements.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_ddrm_thumb.png)

[Image reconstruction with a diffusion model](https://deepinv.org/auto_examples/sampling/demo_ddrm.html.md)

  <div class="sphx-glr-thumbnail-title">Image reconstruction with a diffusion model</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we revisit the implementation of the DiffPIR diffusion algorithm for image reconstruction from zhu2023denoising. The full algorithm is implemented in deepinv.sampling.DiffPIR.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_diffpir_thumb.png)

[Implementing DiffPIR](https://deepinv.org/auto_examples/sampling/demo_diffpir.html.md)

  <div class="sphx-glr-thumbnail-title">Implementing DiffPIR</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This demo shows you how to use our wrapper deepinv.models.DiffusersDenoiserWrapper to turn any SOTA models from the HuggingFace Hub to an image denoiser. It also can be used to perform unconditional image generation or for posterior sampling.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_diffusers_thumb.png)

[Using state-of-the-art diffusion models from HuggingFace Diffusers with DeepInverse](https://deepinv.org/auto_examples/sampling/demo_diffusers.html.md)

  <div class="sphx-glr-thumbnail-title">Using state-of-the-art diffusion models from HuggingFace Diffusers with DeepInverse</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This demo shows you how to use deepinv.sampling.PosteriorDiffusion to perform posterior sampling. It also can be used to perform unconditional image generation with arbitrary denoisers, if the data fidelity term is not specified.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_diffusion_sde_thumb.png)

[Building your diffusion posterior sampling method using SDEs](https://deepinv.org/auto_examples/sampling/demo_diffusion_sde.html.md)

  <div class="sphx-glr-thumbnail-title">Building your diffusion posterior sampling method using SDEs</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we will go over the steps in the Diffusion Posterior Sampling (DPS) algorithm introduced in chung2022diffusion. The full algorithm is implemented in deepinv.sampling.DPS.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_dps_thumb.png)

[DPS – Posterior Sampling with Diffusion Models](https://deepinv.org/auto_examples/sampling/demo_dps.html.md)

  <div class="sphx-glr-thumbnail-title">DPS -- Posterior Sampling with Diffusion Models</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares six approximations of the measurement-matching term used by diffusion posterior samplers.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_noisy_data_fidelity_thumb.png)

[Noisy data-fidelity terms for diffusion posterior sampling](https://deepinv.org/auto_examples/sampling/demo_noisy_data_fidelity.html.md)

  <div class="sphx-glr-thumbnail-title">Noisy data-fidelity terms for diffusion posterior sampling</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This code shows you how to use sampling algorithms to quantify uncertainty of a reconstruction from incomplete and noisy measurements.">![](auto_examples/sampling/images/thumb/sphx_glr_demo_sampling_thumb.png)

[Uncertainty quantification with PnP-ULA.](https://deepinv.org/auto_examples/sampling/demo_sampling.html.md)

  <div class="sphx-glr-thumbnail-title">Uncertainty quantification with PnP-ULA.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates various geometric image transformations implemented in deepinv that can be used in Equivariant Imaging (EI) for self-supervised learning:">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_ei_transforms_thumb.png)

[Image transformations for Equivariant Imaging](https://deepinv.org/auto_examples/self-supervised-learning/demo_ei_transforms.html.md)

  <div class="sphx-glr-thumbnail-title">Image transformations for Equivariant Imaging</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This code shows how to restore a single image corrupted by Poisson noise using Poisson2Sparse, without requiring external training or knowledge of the noise level.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_poisson2sparse_thumb.png)

[Poisson denoising using Poisson2Sparse](https://deepinv.org/auto_examples/self-supervised-learning/demo_poisson2sparse.html.md)

  <div class="sphx-glr-thumbnail-title">Poisson denoising using Poisson2Sparse</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We demonstrate the use of our deepinv.transform module for use in solving imaging problems. These can be used for:">![](auto_examples/transforms-equivariance/images/thumb/sphx_glr_demo_transforms_thumb.png)

[Image transforms for equivariance & augmentations](https://deepinv.org/auto_examples/transforms-equivariance/demo_transforms.html.md)

  <div class="sphx-glr-thumbnail-title">Image transforms for equivariance & augmentations</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the Deep Equilibrium Attention Least Squares (DEAL) model in DeepInverse for both denoising and a simple reconstruction settings.">![](auto_examples/unfolded/images/thumb/sphx_glr_demo_deal_thumb.png)

[DEAL denoising and reconstruction](https://deepinv.org/auto_examples/unfolded/demo_deal.html.md)

  <div class="sphx-glr-thumbnail-title">DEAL denoising and reconstruction</div>
</div>
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