PSF Estimation
Description
This is the accompanying dataset for the publication Adrian Shajkofci, Michael Liebling, “Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy,” IEEE Transactions on Image Processing, vol. 29, pp. 5848-5861, 2020.
Publications based on this data must cite the above paper.
BibTeX Citation:
@ARTICLE{shajkofci.liebling:20,
author={A. Shajkofci and M. Liebling},
journal={IEEE Trans. Image Proces.},
title={Spatially-Variant {CNN}-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy},
year={2020},
volume={29},
number={},
pages={5848-5861},
doi={10.1109/TIP.2020.2986880}}
In the archive, you will find :
- - Trained models for PSF estimation and deconvolution
- - Synthetic training dataset of cells and beads
- - Stacks of multi-channel fluorescence microscopy images of HeLa cells, rat brain cells, beads and plant cells to test the PSF estimation tool, deconvolution algorithm or auto-focus algorithm.
- - Stacks of tilted grid (3, 6 and 9 degrees) using astigmatic lenses for depth estimation.
The code for running the models is available here:
https://github.com/idiap/psfestimation
Reference paper
A. Shajkofci and M. Liebling, "Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy," in IEEE Transactions on Image Processing, vol. 29, pp. 5848-5861, 2020, doi: 10.1109/TIP.2020.2986880.
Ethical compliance
The post-mortem stained and fixed tissue slices whose images are included in this data set were reused from experiments approved by the EPFL ethics committee.
Funding
This work was supported by the Swiss National Science Foundation under Grants 206021_164022 “Platform for Reproducible Acquisition, Processing, and Sharing of Dynamic, Multi-Modal Data” and 200020_179217 “COMPBIO: Computational biomicroscopy: advanced image processing methods to quantify live biological systems”