So the key to really good noise reduction is here.
Do you realise what it actually is that is happening? If I understand your procedure, you seem to be using the low resolution color separations from the R, G, and B, filtered sensels from the Bayer CFA data. These separations are sampled at every 2nd sensel, therefore at 50% of the Nyquist frequency. You then reduce noise at those spatial frequencies, and then do the Demosaicing on that frequency attenuated/filtered data.
Doing so with Images Plus, will unfortunately mean that Color management is out of the window, but one does have the benefit of doing the noiseband filtering at a high numerical precision, and subsequently converting the gamma to something like 1/2.2 will be able to use that precision for a slightly more accurate result before rounding to 16-bit channels.
It also means that you sacrifice accuracy in luminance demosaicing (we've seen the zipper artifacts), which would not happen with a proper dedicated noise removal tool which masks the detail, and only removes a tweakable amount of noise from low spatial frequency areas. I suppose a profiled (I don't have a profile for the cameras you showed samples of, so I can't show the optimal result) NeatImage run would achieve something similar. Also TopazLabs Denoise plugin delivers very useful results.