[irwjager (Ivo)]

Originally Posted by multiweb

Hi Ivo, you're talking about software binning? I thought hardware binning was different.

You're absoluetely right - I'm talking about software binning and I should've mentioned that. IMHO hardware binning should be avoided, as it is best done in software, giving you more flexibility.

On a camera you lose color information for an OSC and resolution for a mono as well but apparently I hear your SNR is better because signal ratio to readout noise increases too. Which is one real advantage.

You're bringing up a very interesting point here which is very relevant to this discussion as well. A CCD with an on-chip Color Filter Array (e.g. a 'color camera') will need to somehow conjur up a lot of missing data, since pixels are distributed over different color channels. In the case of the most used CFA, the Bayer filter (RGB), this means that only 1/3rd of the data is available and 2/3rds needs to be interpolated (aka 'Demosaicing' or 'Debayering'). This process too is a great source of noise, and it's usually noise of the 'worst' kind; correlated noise.

Whereas uncorrelated noise (aka 'random' noise) can be dealt with by the methods posted in this forum or even software noise filters, correlated noise is much harder to deal with because it affects neighboring pixels as well.

Worse, most demosaicing algorithms assume a correlation between the brightness levels of the green channel and the blue and red channel, which means that noise in any of these channels may 'migrate' into other channels. This makes any traditional software noise filtering useless and can really confuse stacking software and quality estimation algorithms.

Some Demosaicing routines are worse than others for noisy images and I encourage anyone to try as many as possible to see what works best. Funnily enough I've found that the best algorithms for natural photography sometimes perfom the worst when it comes to noisy astrophotography images. This usually has to do with high-frequency detail detection, which, when given noiseless input, works as intended and reduces color artefacts (blue, yellow, green, purple fringes and artefacts). However the high-detail frequency detection is often thrown off by random noise (which is by it's very nature high frequency).

Other algorithms look at neighbouring pixels to aid in edge and/or gradient detection. If such a pixel is incorrectly set due to noise, the effect can be a cascade of errors in the debayerd output image affecting neighbouring pixels, introducing yet more artifacts and thus correlated noise.

Nebulosity, for example, uses a demosaicing algorithm that is very simple - no serious natural photographer would touch it with a barge pole. However, it's exactly its simplicity that makes it a much better candidate for dealing with noisy images than the more complicated algorithms found in the various photo processing suites.

But if you have skyglow to start with and you bin 2x2 then flatfield/calibrate would you get a better result than no binning+calibration then software binning?

The reason why you would choose software binning over hardware binning is that, when using hardware binning, your image gets saturated quicker and you need to stop your exposure sooner to avoid ending up with a completely white image (due to skyglow).

A longer exposure gives you a better signal-to-noise ratio (the signal goes up linearly with the exposure, the noise goes up as the square root of the exposure). Also my rule is to only throw away data at the last possible moment, only if I get something for it in return and, if I can help it, on my terms under my control.

When I process images, at every step, I actually envision what I'm doing to my pixels and why - it's not just a matter of 'what looks good at the moment'. It really does pay to learn about the algorithms behind the operations you perform on your image - it can help you combine processing steps in your head and think ahead.

Sorry, went a bit on a tangent there...