elsa is a modern, elegant C++ library intended for use in tomographic reconstruction. Currently, operators are implemented for the forward model of X-ray Computed Tomography (the X-ray transform). Other imaging modalities can be supported by implementing appropriate operators.
- Code repository: https://gitlab.com/tum-ciip/elsa
- Documentation: https://ciip.cit.tum.de/elsadocs/
- License: Apache License 2.0
- DOI: 10.21105/joss.06174
Robotic sample holder
Our software package for a robotic sample holder is designed to enable robotic X-ray computed tomography, building on ROS. It offers support for path planning, collision detection, calibration, and generation of arbitrary acquisition trajectories.
- Code repository: https://gitlab.lrz.de/IP/robotic-sample-holder/robotic-sample-holder
CWFA contains code for a conditional wavelet flow architecture for 3D reconstruction of XLFM images. This code is associated with our preprint arXiv:2306.06408.
WindowNet contains code for learnable windows for chest X-ray classficiation. This code is associated with our publication WindowNet: Learnable Windows for Chest X-ray Classification.
- Code repository: https://gitlab.lrz.de/IP/windownet
FusionM4Net contains code to perform multi-modal multi-label skin lesion classification. This code is associated with our publication FusionM4Net: A multi-stage multi-modal learning algorithm for multi-label skin lesion classification.
- Code repository: https://github.com/pixixiaonaogou/MLSDR
SLNet / XLFMNet
SLNet / XLFMNet contains code to perform real-time light field 3D microscopy via sparsity-driven learned deconvolution. This code is associated with our publication Real-Time Light Field 3D Microscopy via Sparsity-Driven Learned Deconvolution.
LFMNet contains code to reconstruct a 3D confocal volume from a 4D light field image. This code is associated with our publication Learning to Reconstruct Confocal Microscopy Stacks From Single Light Field Images.
oLaF is a flexible and efficient Matlab framework for 3D reconstruction of light field microscopy data. It is designed to cope with various LFM configurations in terms of MLA type (regular vs. hexagonal grid, single-focus vs. mixed multi-focus lenslets) and placement in the optical path (original 1.0 vs. defocused 2.0 vs. Fourier LFM designs).