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Context: I am a PhD student in physics and right now I am studying phase retrieval with low illumination

With two different parameters I obtain these two images:

enter image description here

With hight illumination (less noise) and the same parameters I obtain:

enter image description here

The real object is pi. One parameter that I use to estimate the quality of the reconstruction is the Pearson coefficient with the real object.

Problem: I have the same correlation for the first pair of images (with low illumination) because the second one has less noise, but it has a worst resolution.

What is a parameter that describes the fact that, despite noise, the object is more recognizible in the first picture. I cannot take images with other objects, for example to evaluate spatial resolution for different parameters.

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  • $\begingroup$ is that π always in the same place? then you don't need to detect it, just calculate some correlation between the ideal/model and whatever image you are trying to assess. $\endgroup$ Commented Dec 30, 2022 at 15:33
  • $\begingroup$ @ChristophRackwitz what? yes it is in the same place. If I calculate the correlation I have the same one for the first pair of pictures and I want to find a parameter that distinguishes them $\endgroup$ Commented Dec 30, 2022 at 23:32
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    $\begingroup$ ah okay. then perhaps throw a gradient filter on these first two pictures, and on the model, and see how well that correlates then. I'd expect the first one to score better because I can make out some edges that I don't see at all in the second one. $\endgroup$ Commented Dec 30, 2022 at 23:41
  • $\begingroup$ @ChristophRackwitz if you want to write your comment in an answer I will vote it as the best one $\endgroup$ Commented Jan 1, 2023 at 18:27

2 Answers 2

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Since you say the π symbol will always be in the same place, you don't need to detect/locate it.

You can compare per-pixel. You could calculate a correlation score against a model image.

Since the appearance's magnitude seems to vary, I would recommend first applying a highpass (gradient/edge filter). That leaves edges, which approximate what we consider "recognizable" features.

I've applied a Sobel filter to your pictures. You can see the symbol in the first picture is barely visible, but not visible at all in the second. The first picture will give you some response in a correlation, the second one will not.

out 1 out 2

out 3 out 4

These results are below my expectations because you've only provided false-color plots, not source data. The false-color plots don't convert well into grayscale. Turning false-color back into source values is doable but it requires knowledge of the colormap that was used. I will not do that here.

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You may take an empirical approach.
Train a model to detect the $\pi$.
This model basically output the probability of a $\pi$ in the input image.

Then give the output of your algorithm to the model and measure its quality by the probability of the detection model.

The procedure should be:

  1. Create a data set of good and bad samples and some noise (Like low quality reconstruction). You can generate those with many models for image degradation (Blur, Noise, Sampling, etc...).
  2. Train a classifier on the data set.
  3. Tune the probability of the classifier.
  4. Use it as a measure of quality.

I have done this in a project and it worked really well even if the training data set was synthetic.

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  • $\begingroup$ Could the one who -1 explain? I will be happy to discuss. $\endgroup$
    – Royi
    Commented Dec 30, 2022 at 9:55
  • $\begingroup$ I have put a +1, they are only toxic people $\endgroup$ Commented Dec 30, 2022 at 15:24

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