ROCI: Repository of Computational Imaging

ROCI is a collection of minimalistic, high-quality, and self-contained PyTorch implementations of computational imaging algorithms. In terms of applications, ROCI focuses on forward and inverse problems appearing in computational MRI (reconstruction, quantification, synthesis, physics simulations). In terms of techniques, it provides classical signal processing algorithms alongside SoTA representation learning and generative modeling methods. All algorithms are written in PyTorch. This means autograd compatibility and GPU-accelerated compute. Full Changelog: https://github.com/cnmy-ro/roci/commits/v0.1.1