[Shiraz (Ray)]

Originally Posted by Merlin66

Ray,
Obviously we adopt different measures to evaluate SNR in our spectra....
What process do you use for AP images?
(I haven't used it for a while, but I believe Imagej can analyse the image for background SNR).
The sensitivity of the OSC pixels is a mixture of the Bayer filter bandwidth and transmission, the underlying QE of the chip and the eventual De-Bayering algorithm used ( i.e. Bilinear v's VNG (variable number of gradients))

Hi Ken. I would assume that the basic idea is the same, but the implementation would be entirely different, since an image is not a fixed thing - the signal varies with which bit of the image you choose. However, shot noise from the background sky glow should entirely dominate the noise and that is easy. Standard techniques for measuring image SNR involve the correlation between two images of the same scene - the correlated bits are the signal and the uncorrelated bits relate to the noise. However, it is rare for anyone to bother measuring image SNR, since the image appearance is ultimately what matters and the appearance of an image is definitely related to SNR. Re measurement, I have used Nebulosity to select specific 29x29 regions in images and measured local mean and variance for direct SNR comparison (on synthetic images, some of the central region of Helix and an outlying region in 2997). However, for comparative analyses like this one, knowing how the SNR scales with the various parameters is all that matters - absolute SNR would add nothing.

Originally Posted by Camelopardalis

Perfect timing Ray as I've just got myself my first mono chip

My first experiment has been with luminance subs at 1x1 and then RGB subs at 2x2 binning of the same exposure time as luminance. I think that at the 1x1 resolution of my experiment, 2.4"/pixel, the colour data may not be fine enough and many stars are clipped.

I figure I will try 1x1 for RGB next...but given 1/3 the photon flux, the colour exposures would need to be longer than the luminance, unless I misunderstand.

sounds like a plan to me. If the stars are clipped, maybe reduce the exposures - as a first go, possibly try 1x1 RGB at the same exposure as lum and work up from that?
FWIW, I have been using 2x2 binning for RGB with 180sec for RG and 300s for B - to go with lum 1x1 at 300s.

Originally Posted by codemonkey

I was mainly thinking in terms of 2x2 binned RGB "adding complications" as you put it; specifically I was thinking about the annoying artefacts I get around bright stars which is worsened when binning RGB.

Another interesting option might be the possibility of being able to combine the drizzled RGB with the luminance. I have NFI if it'd work out better, but food for thought. Think I'll give it a shot soon.

let us know how you get on. If using PI, You may be able to fix the artefacts around 2x2 stars by using something simple (eg bilinear) as the registration interpolation function or changing the clamping threshold (if sticking with the default).

Originally Posted by RickS

An interesting comparison, Ray.

A few quick thoughts:

With option 3 are you assuming an improvement in read noise from hardware binning? If you're sky limited then software binning should work nearly as well and you'd be able to add the RGB data in to luminance.

Have you thought about the impact of noise reduction? It seems to me that binning is really a dumb form of NR. Dumb in the sense that it blurs all the data, not just the low SNR areas.

I have used option 4 with bright, colourful targets, like M31, and it works brilliantly in that situation.

Wrt Drizzling, it does complicated things with noise (see http://www.stsci.edu/hst/HST_overvie...34.html#385457 if you're interested). In practice, I think it's effectively a noise reduction technique as the correlated noise is less obvious visually.

Cheers,
Rick.

Thanks Rick. That strategy is even smarter - get the best of all worlds - probably need to add in a few short RGB subs to boost the dynamic range - will add it the table.

I chose 2x2 binning as the standard spatial filter simply because it is clear what it does. I know that the hardware implementation leaves a bit to be desired on some chips, but that can be worked around. Other filters would be better, but I am not sure how to quantify what they do to the SNR.

thanks for the drizzle link - again, it doesn't fit easily into a simple analysis like this, but would definitely be worth some follow-up experimentation.

thanks for the input folks. Looks like a bit of experimentation is in order. regards ray