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Spatio-temporal image restoration with entropy-based regularization for application to low-SNR fluorescence microscopy image series.

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IPMI-ICNS-UKE/TDEntropyDeconvolution

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Time-Dependent Image Restoration

For detailed instructions see the Documentation.

About

This program deconvolves microscopy images. The input can either be a 2D single image or a 2D time series, i.e. 2D+t data. If the program is used, please cite the corresponding publication [1]. The 2D-only version is based on [2]. For the deconvolution of a time series, a regularizer in time domain was added. The point spread function can either be given as image input or calculated analytically with the relevant parameters.

Usage

  1. Enter parameters in parameters.json file, details see in the Documentation
  2. Run python main.py

References

[1] L. Woelk, S. A. Kannabiran, V. Brock, Ch. E. Gee, Ch. Lohr, A. H. Guse, B. Diercks, and R. Werner. 2021. "Time-Dependent Image Restoration of Low-SNR Live Cell Ca2+ Fluorescence Microscopy Data". International Journal of Molecular Sciences 22 (21): 11792. https://doi.org/10.3390/ijms222111792.

[2] Arigovindan, Muthuvel, Jennifer C. Fung, Daniel Elnatan, Vito Mennella, Yee-Hung Mark Chan, Michael Pollard, Eric Branlund, John W. Sedat, und David A. Agard. 2013. „High-Resolution Restoration of 3D Structures from Widefield Images with Extreme Low Signal-to-Noise-Ratio“. Proceedings of the National Academy of Sciences 110 (43): 17344–49. https://doi.org/10.1073/pnas.1315675110.

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Spatio-temporal image restoration with entropy-based regularization for application to low-SNR fluorescence microscopy image series.

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