DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning

Differentiable optical simulation for end-to-end cameras.

Scientific computing library for optics, computer graphics and visual perception.

(TPAMI 2025) Invertible Diffusion Models for Compressed Sensing [PyTorch]

Pado: Pytorch Automatic Differentiable Optics

Scientific Computational Imaging COde

Modular and scalable computational imaging in Python with GPU/out-of-core computing.

(TPAMI 2024) Practical Compact Deep Compressed Sensing [PyTorch]

(Tensorflow Version) D-Flat is a forward and inverse design framework for flat optics. Although specially geared for the design of metasurface optics, it may be used for any end-to-end imaging and sensing task.

Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.

Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning

🚀⏱️ Official implementation for "neural space-time model for dynamic multi-shot imaging"

[CVPR'19] End-to-end Projector Photometric Compensation

Official implementation of Neural Lithography (SIGGRAPH Asia 2023)

PtyRAD: Ptychographic Reconstruction with Automatic Differentiation

Keras Implementation of the paper Residual Feature Distillation Network for Lightweight Image Super-Resolution

Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]

3D reconstructions of mm-scale objects from sequences of phone camera images

Repository for ptychography software

Demo for single-pixel imaging using Hadamard functions as sensing basis. Matlab/Python implementations