What are the correct way to calculate the SNR with these images in MATLAB?

1
votes

Currently I am trying to figure out the Signal to Noise Ratio of a set of images as a way of gauging the performance of my deconvolution (filtering algorithms). I Have a set of images like the one below, which show the image, before and after the algorithm:

enter image description here

Now, I have discovered quite a few ways of judging the performance. One of these is to use the formula for the SNR of an image, where the signal is the original image and the noise is the filtered image. Another method, as described by this question, goes about figuring out the SNR from the singular image itself. This way, I can compare the SNR ratios that I get for both images and get an all new altogether.

Therefore, my question lies in the fact that, the resources on the internet are confusing and I do not know about the "correct" way of measuring the SNR of these images and using it as a performance metric.

1
performancematlabimage-processingfilteringsignal-processing
It really depends on what you are trying to compare, and what you deem as "signal" and "noise". In your first method, you are effectively calculating the error(or difference) between image 1 and image 2 where you assume image 2 was tinted by noise but image 1 was not (this is also a sort of signal to distortion ratio). Therefore, this measurement is relative. – GameOfThrows
In the second method based on the link you posted, you are assuming that noise is present in both images but at different levels and you are measuring it against each individual image - or in other words, you are measuring the standard deviation of each individual image, which is not relative. I add that the second measurement is usually used to compare results generated from the same source, while method 1 is usually used to compare the performance of the method that generated image 2 from image 1. – GameOfThrows
@GameOfThrows That's the answer that I was looking for! Put it down and I will accept it. Please include where, you would use both of the techniques to measure the SNR and which image goes where in terms of the equation for SNR. – SDG

1 Answers

2
votes

It really depends on what you are trying to compare, and what you deem as "signal" and "noise". In your first method, you are effectively calculating the error(or difference) between image 1 and image 2 where you assume image 2 was tinted by noise but image 1 was not (this is also a sort of signal to distortion ratio). Therefore, this measurement is relative and it measures the performance of your method of transformation from Original to Target (or distortion technique), but not the image itself. For example a new type of encrypting filter generated image 2 from image 1 and you want to measure how different the images are to work out the performance of your filter.

In the second method based on the link you posted, you are assuming that noise is present in both images but at different levels and you are measuring it against each individual image - or in other words, you are measuring the standard deviation of each individual image, which is not relative.The second measurement is usually used to compare results generated from the same source, i.e. an experiment produces N images of the same object in a controlled environment and you want to measure, for example the amount of noise present at the scene (you would use this method to work out the covariance of noise to enable you to control the experiment environment).