Not at all.
To cite the paragraph just above the figure you took from the document you linked to (emphasis by me):
One of the
key color information is the red channel, which
will help localize the region of interest (ROI) … .
For example, Traffic Sign Recognition (TSR)
applications can identify possible locations of red
colored speed limit signs in the captured image.
Hence, the output generated by RCCC
sensors is almost as detailed as a monochrome
output and still provides the red color
information as shown in Figure 2.
These cameras simply aren't used for anything but getting an intensity and red-color image!
So, albeit there's info about which pixels are more red than others (at quarter resolution, at least), there's no info on whether pixels are more greenish, yellowish or blueish.
So, without further info coming from other pictures, you can only get a greyscale and a red image, and any combination of these, but since there's no info on anything than overall intensity and red intensity, well, no other color info.
Notice that external info might help here – you might train some classifier on a lot of images, and that classifier would then be able to "guess" colors. But that's no better than looking at a black and white photograph of a tree and saying that the leaves are green (because you know that leaves are always green) – that info doesn't come from the image (and hence might be totally wrong; if you don't know the tree, it might be a tree in autumn foliage in bright yellow color).