Deconvolution Toolkit Methods for Computational Optical Sectioning in Life Sciences

Several different deconvolution algorithms have been developed. Some methods are fast and sufficient for systems with low levels of blur. When systems present more challenging levels of blur, more sophisticated algorithms are required that use much more extensive computational resources and time. While these can solve more difficult problems and yield superior image results, the processing time can sometimes present too much of a barrier. ZEN Deconvolution Toolkit offers a range of algorithms, enabling you to choose the best fit for your experimental and post-processing needs.

The Deconvolution Toolkit algorithms have been optimized to make it extraordinarily easy-to-use and high performing. ZEN reads the image’s metadata and automatically determines the point spread function (PSF) of the optical system to adjust the optimal settings for deconvolution. Algorithms are accelerated by more than 10-fold by GPU or even mGPU processing. To optimize time-to-results, all deconvolution algorithms run with ZEN Direct Processing during image acquisition. And the lossless data compression algorithm Zstandard (zstd) further accelerates resource-intensive processing.

Image: U2OS cells labeled for mitochondria (TOM20-mCherry) and microtubules​ (Tubulin-GFP) structures before and after Constrained Iterative Deconvolution.