Good's roughness Maximum Likelihood Estimation
With the Good's roughness Maximum Likelihood Estimation algorithm (GLME) you will see improved handling of very noisy images, in especially STED and confocal data.
It has been introduced in the Huygens release 14.10 and can be activated in the Deconvolution Wizard. The GMLE is available in Huygens Professional, and in the Batch Processor of the Huygens Essential.
The amount of iterations needed by the GMLE to reach the same restoration result as with a CMLE or a QMLE is significantly lower (up to 4 times less iterations), thus saving time. The memory usage however with the GMLE is significantly higher (how much is depending on the size of the image an the occurrence of spherical aberration which will get changing PSF's in the Huygens deconvolution) than for usage with the QMLE and the CMLE algorithms. As Widefield data are often larger than Confocal and STED data the memory usage needed can be a hindrance in reaching a result fast. In those situations, the CMLE and the QMLE are better suited.
For a general overview of all algorithms in Huygens see Restoration Methods.
STED image courtesy of Leica microsystems CMS GmbH, deconvolved with Huygens CMLE algorithm.