Normalized cross correlation via FHT - how can I get correlation score? I'm using the 2D Fast Hartley Transform to do fast correlation of two images in the frequency domain, which is the equivalent of NCC (normalized cross correlation) in the spatial domain.

However, with NCC, I can get a confidence metric that gives me an idea of how strong the correlation is at a certain offset. In the frequency domain version, I end up with a peak-finding problem in the inverse FHT after doing the correlation, so my question is:

Can I use the value of the peak that I find in the correlation image to derive the same (or similar) confidence metric that I can get from NCC? If so, how do I calculate it?
Ok, I finally found the reason for my confusion - thanks to this answer.

There were a number of reasons why I couldn't get my spatial NCC and frequency domain peak values to agree numerically. Some obvious, some subtle, all stupid. For the sake of completeness, here they are:


I had missed an offset and scale factor that was introduced when
normalising my image data after importing the pixel values in
preparation for FHT. 
I was using a predicted maximum value for the
    peak, from my sub-pixel shift calculation instead of the actual
    peak.
I had a low-pass filter in the pipeline, which - for
    performance - I was applying concurrently with the correlation
    operation. So my peak value was scaled by the filter.


Not being mathematically inclined, I was wrongly attempting to "explain" my problem by assuming that using FHT was somehow inherently different to using FFT. After being pointed in the right direction I found the above issues and finally got the results I was expecting.