A Confocal microscope is one type of 3D Fluorescence Microscope in which resolution is increased by rejecting out-of-focus light. This is done by using pinholes with a specific Pinhole Radius and makes it to be an intrinsic 3D microscope: due to the focusing nature of the confocal microscope, it is specially suitable for obtaining 3D images as a collection of intensities at different points of the space.
Confocal images are very well suited for Huygens deconvolution because of two main reasons:
1. they still suffer from blurring, which can be clearly seen when recording bead images
2. in general, confocal images are noisier than widefield images, because light from the object is rejected by the pinhole.
In confocal microscopes, the Nyquist Rate is about one half of that in Widefield Microscopes.
Alternatively, it is also possible to scan the sample with multiple excitation spots in parallel such as with a Spinning Disk microscope.
STED is practically an extension of point-scanning confocal microscopy. The STED beam needs to be focused in the sample to obtain the high intensity needed for stimulated emission, while point-wise detection reduces the out-of-focus light. To make STED work, the excitation focus center and STED intensity null of the doughnut shaped focus need to overlap perfectly. An image can then be obtained by raster scanning the sample, for example with a piezo scanning stage. The size of the pinhole used will determine the depth of focus of the microscope.
Classic Maximum Likelihood Estimation (CMLE) and the default SNR value is 20. We suggest to start with the default parameters and explore other SNR values, for example, to optimize the deconvolution results. You should not think about the SNR as a parameter describing your original image, but as a tunable parameter that controls the deconvolved result. Using a too large SNR value might be risky when restoring noisy originals, because you could be just enhancing the noise. A noisy confocal image can have SNR values lower than 20. Another option is testing the Good's roughness Maximum Likelihood Estimation (GMLE) algorithm to improve the results. The GMLE is suited for noisy images, as confocal or STED.
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 Remko R.M. Dijkstra, Design and realization of a CW-STED super-resolution microscope setup, Master Thesis , University of Twente, 2012