Not necessarily, but it's completely different from what Dan Margulis was preaching. Dan is suggesting the blurring of the Chroma channels, while Arthur is focusing on removing noise at the Raw stage before demosaicing (which normally would turn single pixel noise into multiple pixel blobs of color). That's what Astrophotography tends to do, improve the signal to noise ratio as much as possible before demosaicing.
The challenge is in reducing the noise (not only chromatic noise), but not affect the capability to demosaic actual resolution at the same time.
Raw converters like DxO Optics Pro, RawTherapee 4.x, and Capture One Pro 7, offer noise reduction and Luminance detail control that works at the Raw data level, while being Color Managed. The program Arthur uses (ImagesPlus) is not color managed, and also a bit too specialized for regular photographic use. Some of the noise reduction functions of ImagesPlus are adaptive (edge preserving), and can be tweaked at the Bayer CFA level, before the demosaicing, and before leaving the linear gamma space.
A very high quality (and Color Managed !) alternative for ImagesPlus, is PixInsight. However, that's also not very easy to use, mainly because it's documentation is not available for many of the functions (although a lot can be learned from their forum members), and it is more than just an image processing application. It's also a program development environment for image processing, mainly focusing at the needs of astronomers, but it is very well programmed by a small team of dedicated programmers/astronomers.
It's a bit different, not quite what Dan did, from your explanation and not quite what you describe. I must have failed to communicate it properly.
This method does not really let me remove noise before de-mosaicing. I am doing my steps after de-mosaicing. What I guess my method shows is that the de-bayer algorithms are nowhere near as advanced as the latest de-noise algorithms. I am also using them in different dimensions. Thinking this way is more natural to someone who has matrix math training and/or someone who has OLAP database training. It's more of a jump for others. It can still be done.
I am using the power of a modern computer with modern noise reduction in 12 dimensions. In camera processing cannot touch this. For example my software is 64bit multi-threaded on a quad core 2.8 GHz with 8 Gigs RAM. Forget tiny chips in your camera for JPGs.
What we assume our de-bayer method does is fill in holes from missing pixels on each color to re-create full image by color layer just as the old film color layers did. I would say for Low ISO whatever they are doing seems to work. The problem comes to High ISO when we see very harsh jumps in pixel values (noise) or wandering color patterns. Clumps of colors without NR. This is a processing failure not necessarily a shot S/N problem.
So here it is again. My process uses the best NR programs on each color channel. I happen to use Images Plus for a very clean (random) noise conversion. I'm sure other programs like what Bart mentions work well too. The TEST for working well is leaving a fine random pattern of noise. There should be no clumps of color. When you seperate out the channels you should still see fine random noise that looks like fine film grain.
Attached is a screenshot of Blue, Green and Red channels shown in B/W. The noise is strongest on Red, then Blue, then Green. Your favorite N/R programs noise ninja, topaz de-noise, neat image, whatever will do wonders on this. Use it on RGBL. The noise also looks the same in CMYL. Do it again. Re-combine each split RGBL, CMYL. Combine both in layers or using average functions.
What you have done is take slices through a 12 dimension matrix, wiped out pixel to pixel variances in each dimension, leaving a very natural looking file with real depth
. Heavy NR as your raw converter programs do it leave a very artificial looking image. With their methods your choice is leave noise in or look artificial.
So by Bart's description Dan's method is not the same as Arthur's method. The name Arthur's method stays.