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.
As an extra bonus you need not give a numerical value of the Signal To Noise Ratio, but only need to select an estimate ranging from 'Noise free' to 'Good', 'Fair' and 'Low', this compares roughly with SNR of 40 to 30 for 'Noise free' ranging to 10 or a bit lower for 'Low'.
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.