Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics

Resources for phase recovery (also called phase imaging, phase retrieval, or phase reconstruction)

PyTorch library for solving imaging inverse problems using deep learning

BART: Toolbox for Computational Magnetic Resonance Imaging

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

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

A curated list of resources on holographic displays.

Scientific Computational Imaging COde

Rank Minimization for Snapshot Compressive Imaging (TPAMI'19)

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

Official Demo Code for "Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms"

(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.

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

DFlat 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.

A Julia project demonstrating the fast f-k migration algorithm.

Plug-and-play Algorithms for Large-scale Snapshot Compressive Imaging (CVPR 2020, Oral)

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.

Deep Learning for Video Compressive Sensing

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

Plug-and-Play Algorithms for Video Snapshot Compressive Imaging (TPAMI 2022)