Quick Maximum Likelihood Estimation
There are very compute-intensive situations, for example when deconvolving Widefield 3D-Time Series, in which faster Restoration Methods can be used. In these cases you may consider to use Quick Maximum Likelihood Estimation (QMLE) which is faster than the Classic Maximum Likelihood Estimation (CMLE) and will give excellent results as well.
The results of this algorithm depend mostly on the number of iterations. When the number of iterations is not directly supplied Huygens will choose it based on the the image, Signal To Noise Ratio (SNR) and acuity. In the deconvolution wizard the SNR and acuity are the main ways of choosing these values.
The QMLE is roughly five times more efficient than the CMLE while it takes also slightly less time per iteration. So ten QMLE iterations are equivalent to fifty iterations in CMLE.
While CMLE is superior in handling low Signal To Noise Ratio (SNR) data, like low light level confocal images, it is slower that QMLE. In principle CMLE with a Signal To Noise Ratio > 60 converges to the same result as QMLE gives you, but after many more iterations. In short, for good quality widefield images QMLE is the best choice.
The QMLE is available in Huygens Professional, Huygens Essential and Huygens Core.
For a general overview of all algorithms in Huygens see Restoration Methods.
Widefield image courtesy of Dr. Monica Pons, IBMB Madrid, Spain, deconvolved with Huygens QMLE algorithm.
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