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)

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.

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

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.

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

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

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

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