Stars
10 Lectures on Inverse Problems and Imaging
AI/DL scripts to remove stripe artifacts from the tomographic data
A library of GPU-enabled data processing and reconstruction methods for tomography
Python routines to compute the Total Variation (TV) of 2D, 3D and 4D images on CPU & GPU. Compatible with proximal algorithms (ADMM, Chambolle & Pock, ...)
A library for painless 3D tomographic reconstruction
A toolkit for semantic segmentation of volumetric data using PyTorch deep learning models
Software to generate 2D/3D/4D analytical phantoms and their Radon transforms (parallel beam) for image processing
TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software
The set of CPU/GPU optimised regularisation modules for iterative image reconstruction and other image processing tasks
Programming examples (C/C++/Fortran), HPC oriented (MPI, OpenMP, libraries).
low dose CT simulator to yield low dose CT images from high-dose one
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Ring-reduced reconstruction model for X-ray Computed Tomography
A deep learning approach for stripe noise removal
This package contains code accompanying the manuscript of "On-the-Fly machine learning for improving image resolution in tomography".
Morphological snakes for image segmentation and tracking
An implementation of SIFT on GPU with OpenCL
GPU accelerated pre-filtered cubic b-spline interpolation using CUDA
Michigan Image Reconstruction Toolbox (MIRT) - Matlab version
Image augmentation for machine learning experiments.