# deepinv

> DeepInverse is the leading open-source PyTorch-based library for solving imaging inverse problems with deep learning. It provides imaging operators, pretrained reconstruction networks and denoisers, plug-and-play and unfolded optimization, sampling algorithms, training losses and datasets.


deepinverse contributors 2026

## Pages in this subsection

- [Optimization](https://deepinv.org/auto_examples/optimization/index.html.md): <div id='sg-tag-list' class='sphx-glr-tag-list'></div><div class="sphx-glr-thumbnails">
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_3D_denoising.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_epll.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_TV_minimisation.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_custom_prior.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_wavelet_prior.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_multispectral_demosaicing.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_patch_priors_CT.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_poisson_mlem.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/optimization/demo_dip.html.md): New to DeepInverse? Get started with the basics with the

## Optional

- [Top-level llms.txt](https://deepinv.org/llms.txt): Complete documentation index.