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Data supplement to: Quality control of image sensors using gaseous tritium light sources

Citation

McFadden, David; Amos, Brad; Heintzmann, Rainer (2022), Data supplement to: Quality control of image sensors using gaseous tritium light sources, Dryad, Dataset, https://doi.org/10.5061/dryad.cvdncjt5f

Abstract

In the article “Quality Control of Image Sensors using Gaseous Tritium Light Sources” (https://doi.org/10.1098/rsta.2021.0130) we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (betalights). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.

Methods

The raw data consisting of stacks of calibration images, spectral data, and photodiode readings were acquired according to the method described in the publication.

Tabular data on spectral responsivity were manually read from graphical plots that were included in the camera brochures or quality control reports.

Some images may have been converted from one lossless format to lossless tiff files in order to read and process the data. Some image sequences may have been converted to a 3D stack and vice versa.

Usage Notes

All processing is done in the .ipynb notebook script (https://doi.org/10.5281/zenodo.5738630). This should be consulted for details about the processing environment and processing steps. The individual steps are commented and figures are saved to an output directory. For convenient viewing, there is also an HTML rendering of the notebook.

The "data.zip" archive should be unpacked. When "evaluation_notebook.ipynb" is run, it expects to find the "data" subdirectory.

run_directory/
├── evaluation_notebook.ipynb
└── data/
    ├── attenuation_experiment/
    └── ...

The notebook uses our publically available "NanoImagingPack" library, available at https://gitlab.com/bionanoimaging/nanoimagingpack/ . Installation instructions are provided there. The package has some additional dependencies that are available from Anaconda. The script was run with code on the following commit:

489080ad8981110f0e1ac043f9952a86827afeb

The included readme file describes the individual files in more detail.

Funding

Deutsche Forschungsgemeinschaft, Award: 1278 Polytarget, Project C04