What was unexpected for me (if I understood correctly) is that the noise of the optimal combination is actually lower than any of the original signals.
One question, how big are the "regions" of the image that you use to determine the ideal factors for every channel? Is there an optimal value here (thinking that at some point you don't gain much by using smaller areas)?
Sometimes I speak about noise when I mean SNR. The optimal combination of two signals always provides a new signal with improved SNR over all the input signals. For instance the optimal mix of the two upper images produces the lower image. The contribution of the noisier input signal is not huge but exists (i.e. Signal 1, the noisiest one, "helps" Signal 2 to become less noisy):
I think I set a 5px gaussian blurr in determining the average signal for every channel. This was just a simple test to check how much improvement we could expect. The algorithm can be refined by fine tuning that blurring radius and specially by introducing optimium weighting in photon noise dominant áreas (the optimal weight formulas remain the same but calculating k and k' will differ). But I don't think the improvement will be gigantic over what I already achieved.
Another possible improvement I want to check is the possibility of using the highest SNR signal, but adjusting its luminance to be more perceptually correct. For instance the cockpit glass is much cleaner in the optimized mix, but it should produce a darker gray tone. By locally correcting exposure we can have the best of the two worlds (SNR and correct luminance).
Regards