DeepInverse: a PyTorch library for imaging with deep learning

DeepInverse is a PyTorch-based library for solving imaging inverse problems with deep learning.

Github repository: deepinv/deepinv.

Featuring

• Large collection of predefined imaging operators (MRI, CT, deblurring, inpainting, etc.)

• Training losses for inverse problems (self-supervised learning, regularization, etc.).

• Many pretrained deep denoisers which can be used for plug-and-play restoration.

• Framework for building datasets for inverse problems.

• Easy-to-build unfolded architectures (ADMM, forward-backward, deep equilibrium, etc.).

• Diffusion algorithms for image restoration and uncertainty quantification (Langevin, diffusion, etc.).

• A large number of well-explained examples, from basics to state-of-the-art methods.

Lead Developers

Julian Tachella, Dongdong Chen, Samuel Hurault, Matthieu Terris and Andrew Wang.