State-of-the-art algorithms

DenoisEM offers several state-of-the-art denoising and deconvolution algorithms such as non-local means, BLS-GSM, Tikhonov deconvolution, etc.

User-interactive parameter optimization

Automated parameter estimation and common ImageJ tools allow for fast parameter optimization.

Fast denoising of large-scale volumes

DenoisEM has a GPU accelerated back-end that ensures massive parallel computing.

Scalable and reproducible

The GPU accelerated back-end and meta-data storage allow for scalable and reproducible image denoising.

References

DenoisEM is developed by the TELIN department at Ghent University and the Bio Informatics Core at VIB. The GPU backbone is driven by Quasar, an in-house programming language of the TELIN department.

If you use our plugin for your work, we ask to acknowledge the following reference:

J. Roels, F. Vernaillen, A. Kremer, A. Goncalves, J. Aelterman, H. Q. Luong, B. Goossens, W. Philips, S. Lippens, Y. Saeys, "DenoisEM: An Interactive ImageJ Plugin for Semi-automated Image Denoising in Electron Microscopy", bioRxiv 644146; doi: https://doi.org/10.1101/644146