Dear Natalya,
for calculating the signal-to-noise ratio, you need to know the "ground
truth", i.e., what the image would be without noise.
There are essentially two approaches for this:
(1) Take a noise free image and add synthetic noise (preferably, with
the same characteristics as the noise of real images; for photos with
digital sensors the noise should be shot noise (Poisson statistics) +
readout noise (roughly Gaussian) + dark current variations (best taken
from a dark field with the same sensor).
(2) As a reference image, you can use the average of many images of the
same object (of course, without any lateral displacements due to
vibrations, etc.). By averaging, much of the noise will cancel out. If
dark current plays a role, you should subtract an average many dark
fields. Best do these operations in 32-bit mode, since it may result in
slightly negative values in low-intensity regions.
When subtracting this low-noise result from the noisy single exposure,
not that there will be a (roughly) constant offset, which should be
removed before evaluating the noise.
[A dark field is a photo with no light intensity reaching the sensor,
but the same exposure time. You can use a black lens cap.]
Best,
Michael
________________________________________________________________
On 29.03.25 16:46, Наталія Тулякова wrote:
Hi, Michael.
Thank you very much for the answer.
I have developed nonlinear filtering algorithms and implemented them as
plugins, taking one of the freely available plugins described in the
package ImageJ as a prototype. I want to compare the filter de-noising
efficiency for some of the test 2D gray-scale images. But I can only
calculate the difference between the test and filtered images using Image
Calculator, and to obtain the total mean value (Analyze - Measure). Is it
possible to obtain the filter efficiency estimation, for example, by means
of “Peak Signal-to-Noise Ratio”, using ImageJ program?
Yours sincerely, Nataliya
вт, 25 бер. 2025 р. о 13:30 Michael Schmid <[email protected]> пише:
Hi Nataliya,
both, Median and Median 3D use a circular support (circular Kernel).
I noticed that Median 3D (and the other 3D filters) use a slightly
different definition of the radius.
For the 3D filters, sometimes one needs to add a small number like 0.5
to the radius of the 2D filters.
For a 2D image (no stack), Median 3D with radius=2.5 in x&y and the
"usual" (2D) Median with radius=2 do exactly the same (except near the
edges, see below).
At some radius values. the behavior is the same for the 2D and 3D
filters (e.g. radius=10.5 and 14.5), so there is no simple rule.
I think that eventually the 3D filters should be modified to use the
same definition of the radius as the 2D filters, the one also used for
Process>Filters>Show circular masks.
The remaining difference is the handling of the edge pixels.
The 3D filters consider the out-of image pixels as nonexistent. Thus,
when calculating the median near the edge, the 3D median uses fewer
pixels. The 2D filters (the "usual" Median, Mean, etc.) assume that the
out-of-image pixels are the same as the nearest edge pixel, and the
mean, median, etc. is always calculated over the same number of pixels
(except for float images with NaN = Not a Number).
The latter convention (assuming repeated edge pixels) is the usual
convention in ImageJ, also for Gaussian Blur, and the Process>Binary
functions.
Hope this helps,
Michael
________________________________________________________________
On 22.03.25 12:32, Наталія Тулякова wrote:
Dear colleagues.
I am interested in filtering 2D images. The ImageJ program has two median
filters in the "Process-Filters" menu item. I have not found any
documentation explaining how "Median 3D" works. Median 3D provides better
results than Median. What is the difference between them for 2D image
processing? Does "Median 3D" use a square window while "Median" uses a
circular window?
With best regards, Nataliya
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