So you are applying a low pass filtering even before SNR calculation. To me that doesn't seem the right thing to do as it will disturb the image signal and noise relationship in the actual data.

Which SNR respone? I don't understand.

The SNR is not obtained from the image, but from the SNR response (i.e. the SNR curves that provide the SNR in that sensor for each exposure value).

The only reason for the blurring is to obtain the average exposure in the pixel's surrounding area. A single pixel's SNR cannot be measured unless you have a before/after noise addition pair of images, and even in that case is irrelevant since the observer doesn't perceive individual pixel's SNR, but local SNR. The local averaging (blurring) provides this average perceived exposure that we just need to translate to the SNR curve of the sensor to obtain the average local SNR.

I don't think that is technically the correct formula to be used. Remember, I asked where is the "signal" in the normalization process. Can you please comment where or what is the signal component in this equation?

That formula is the extrapolation of the 4 pixels binning formula, very well explained in Emil's article.

Joining 4 pixels in one improves SNR by 2, the scaling factor:

(4/1)^0,5=2

The scaling factor from a X Mpx camera to a 12,7Mpx camera is:

(X/12,7)^0,5

The formula I showed is just this scaling in dB.

Perhaps the

tutorial about measuring the SNR curves can help to understand the process.