Computational Imaging Python Packages

deepinv

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

collageradiomics

Python implementation of COLLAGE texture features from BRIC & INVENTLAB.

ptyrad

PtyRAD: Ptychographic Reconstruction with Automatic Differentiation

deeplens-core

Differentiable optical lens simulator for end-to-end computational imaging.

scico

Scientific Computational Imaging COde

pysera

PySERA – Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics

tike

No description available

dflat-opt

D-Flat is a forward and inverse design framework for flat optics. Although specially geared for the design of metasurfaces, it may be used broadly for end-to-end imaging and sensing task.

pyxu

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

odak

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

xdesign

Tools for designing x-ray phantoms and experiments.

pyradiomics-fix

Radiomics features library for python

pyradiomics-cuda

CUDA-accelerated fork of PyRadiomics - a Radiomics features library for python

collageradiomicstypes

CoLliage Implementation

collageradiomics-types

CoLliage Implementation

topology-radiomics

Topology Radiomics by INVENT and BRIC lab

pado-optics

Pado: Pytorch Automatic Differentiable Optics

pycsou

Library for solving linear inverse problems with state-of-the-art proximal algorithms.

phaserem

Weapon of choice for electron ptychographic reconstructions

pyradiomics-bhklab

Radiomics features library for python

collageradiomics-core

Python Implementation of the CoLlAGe radiomics descriptor. CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.

collageradiomicscli

Python Implementation of the CoLlAGe radiomics descriptor. CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.