Signal to noise ratio is meaningful when receiving morse code but when based on
the value of the single most intense pixel in a 6000x6000 image maybe far less
informative.
.
Any suggestions for a measurement of image quality based on a larger and
therefore more representative population of pixels.
Jeremy Adler
===============================================
B i o V i s P l a t f o r m of Uppsala University
Light & EM microscopy / FlowCytometry & Cell Sorting / Image
Analysis
===============================================
Jeremy Adler PhD - Senior research engineer
Light, Confocal Microscopy, Image Analysis
E-mail: [email protected]
0739 188170
www.uu.se/biovis
Dag Hammarskjölds v 20
751 85 UPPSALA, SWEDEN
http://biovis.uu.se/
===============================================
-----Original Message-----
From: Michael Schmid <[email protected]>
Sent: Monday, March 31, 2025 12:28 PM
To: [email protected]
Subject: Re: Median 3D filter
Hi Nataliya,
just see the formula for PSNR:
https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
You can get the mean squared error (MSE) by calculating the square of the
difference image (which must be a 32-bit image), Process>Math>Square
Then take the mean of the squared deviation, see Analyze>Measure and
Analyze>Set Measurements...
You can also get the maximum of the original (the signal) with
Analyze>Measure.
If you use a macro, Analyze>Measure would be replaced with the mean and max
values obtained via
getStatistics(area, mean, min, max);
Michael
________________________________________________________________
On 31.03.25 10:59, Наталія Тулякова wrote:
> Hello, Michael.
> I have implemented my own non-adaptive nonlinear (robust) filtering
> algorithm as a plugin.
> I use some noise-free images (such as "Barbara", "Lena" and artificial
> RSA
> (radio) images). I use the ImageJ command "Process - Noise" and
> simulate salt and pepper and Gaussian noise with different variance. I
> found the plugin for Poisson noise. But I can only calculate the total
> difference between the pure test image and the image obtained after
> the denoising algorithm. I use the commands "Process - Image
> Calculator" and "Analyze - Measure". I mean is it possible by means of
> ImageJ to calculate PSNR? Maybe there is a plugin for this?
> Thanks.
> With best regards, Nataliya
>
>
> пн, 31 бер. 2025 р. о 11:19 Michael Schmid <[email protected]> пише:
>
>> 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
>>>>>
>>>>> --
>>>>> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>>>>
>>>> --
>>>> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>>>>
>>>
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>
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