Originally posted by robjmitchell Noise is additional random data of limited amplitude that sits on top of the image data. The current practice of blending/averaging noise both destroys detail by spreading data between pixels while loosing dynamic range as the black floor is raised and a lower white level is clipped. However there is no reason you cannot recognise the additional noise data and subtract it back to the original curve at the pixel level, ideally prior to amplificaton. The KP seems to be doing this.
There is though. When reading out the signal directly from the sensor, the electric signals do not come carrying tags 'I am noise' and 'I am signal'. While some of the noise might be easily recognizable as noise, a lot of it will not be. Many of the less sophisticated types of noise reduction use thing like convolutions with kernels (aka moving averages aka low-pass filtering) or techniques such as combinations of dilating/eroding, but these are techniques that do not necessarily retain all of the image details, because they attack the signal and the noise (using the fact that noise often has no or very weak correlations across the image, but so does anything with high local contrast). There are cases in which noise can be identified because it is static across mutliple pictures. Dark frame subtraction works for that. But any electronics (and any optical system) has to deal with many different noise types, amongst which there are types (such as shot noise) that cannot reliably be separated from the original image signal. The more sensitive our cameras get, the it is to have good SNR (signal-to-noise ratio). Pentax is usually very good at addressing this, which is one of my main reasons to like pentax (the other being small lenses and the trickery they like to perform with the shake reduction system: astrotracer, AA-simulator, pixel shift).
TLDR: It is never possible to get rid of all the noise in a signal without losing some of the signal, although for some special cases it is possible to get rid of some special types of noise.
PS: a good non-destructive way to reduce noise is by taking multiple images and averaging them. The noise should go down with the square root of the number of pictures taken (a factor of 2 for averaging 4 images, a factor of 4 with 16 images, etc). That is of course difficult (ie, impossible) to do on moving subjects.