coder0856/deepinv

PyTorch library for solving imaging inverse problems using deep learning

 

Introduction -------Deep Inverse is an open-source pytorch library for solving imaging inverse problems using deep learning. The goal of deepinv is to accelerate the development of deep learning based methods for imaging inverse problems, by combining popular learning-based reconstruction approaches in a common and simplified framework, standarizing forward imaging models and simplifying the creation of imaging datasets.

With deepinv you can:

• Use deep learning for solving your inverse problem. You only need to create a physics class that captures your imaging problem. You can try self-supervised learning, unrolled architectures, plug-and-play methods with pretrained denoisers and uncertainty quantification!
• Test new deep learning-based methods on various standard inverse problems (MRI, CT, deblurring, super-resolution, inpainting, colorization, etc.) and compare with existing state-of-the-art methods.
• Create and share datasets, which can be seamlessly evaluated by other users.

Read the documentation and examples at https://deepinv.github.io.

Install

(To be updated, the first stable release will come soon)

Contributing -------

The preferred way to contribute to deepinv is to fork the main repository on GitHub, then submit a "Pull Request" (PR).

Finding help

(To be updated)

Citing DeepInv

If you use deepinv in a scientific publication, please cite the following paper

(To be updated)