How to estimate the signal to noise ratio?
The SNR describes the proportion between signal and noise in the image. The deconvolution algorithms require the SNR of the image as an input parameter. If the deconvolution algorithms are provided with an SNR value that describes the image accurately the restoration will be optimal. However, if the provided SNR value is too high, the image noise will be amplified. On the contrary, if the SNR value is too low, the noise will be attenuated at the cost of the final resolution of the image. Therefore, the SNR can also be thought of as an artifact limiter as far as the restoration is concerned.
In practice the process of estimating the SNR can be started out by calculating the number of photons in the image, or by rule of thumb reasoning. Subsequently one can run the restoration with that first SNR value and inspect the result for artifacts and residue background. The restoration can be run again with a higher SNR setting (say 30%-50%) and perhaps a higher background if everything looks fine after the first run. If you have done this a previous time for a similar image then the same SNR value can be used.
In many confocal images the SNR is due to photon noise. If the background is mostly zero with spikes here and there, then these spikes are probably single photon events. From the height of the average small spike one can roughly estimate how many gray levels correspond with one photon. If, for example, such a single photon event has an intensity value of 5, then the maximum intensity of the image (for example 255) corresponds with 51 photons. The SNR around the maximum intensity area will be: square_root(51) = 7.
With a good confocal image, and when using an 8-bit converter, one can easily get into a situation where one gray level corresponds with more than one photon. In such case, the above procedure might fail, but one could still start a run with SNR 20-30 and increase it later on.
Images from widefield microscopes equipped with 12-bit CCD cameras usually have an SNR in the range of 40-60. These kinds of images can also be deconvolved with the fast Quick-MLE algorithm for low noise images.
Some microscopes are equipped with photon counters like avalanche photo diodes. In this case the SNR is simply the square root of the brightest part of the image.
The Huygens Remote Manager is equipped with an automatic SNR estimator free to use here
(registration needed).See Set The Signal To Noise Ratio.
Keywords: estimate SNR MLE
Categories: Faq Deconvolution, Faq Microscopy, Huygens Faq, Imported Faqs
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