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Old 21-07-2014, 03:01 PM
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Shiraz (Ray)
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Join Date: Apr 2010
Location: ardrossan south australia
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thanks for the comments and discussion.

the demo image was formed when I wanted to isolate noise from signal structure in order to test the predictions of the model against what happens with real image data. I used 15 evenly illuminated lights (no structure) and then calibrated and stacked them as normal, with full flat/bias/dark calibration. When I wanted to investigate dithering, there was a need to move the noise pattern around in the lights so that, when they were stacked without alignment (nothing to align on), the noise would not be correlated from sub to sub - just like you get with dithering, only in this case the frame is fixed, but the noise was moved, rather than the noise being fixed, but the frame moving. It is not a practical method for real image processing, just something I had to use to simulate the effects of dither in featureless light frames.

Rolf, if you use dither, there is probably a lot of merit in removing the pixel scale noise in the flats with spatial filtering. I had been thinking along similar lines of a dual approach to flat fielding with one heavily smoothed flat from each imaging night to remove vignetting and a one-off separate master flat, prepared with even illumination and a lot of flats, to deal with fixed pattern noise and applicable to all images. Be very interested to hear how you get on with smooth flats to remove vignetting and dither to remove FPN. Also, if you are getting some additional noise from using full calibration, suggest that you can improve that with more flat, bias and dark data - the model suggests that in all cases, calibration is ultimately the best way to go. However, it also shows that you may need really high quality calibration data to get to the point where there is some extra SNR from calibration. In the end I guess that it comes down to which approach is more efficient and that will depend on the FPN in your camera. I also suspect that an outstanding problem is that darks contain a subset of warm pixels that generate significant current. You can remove the fixed component of that current by subtraction, but the noise associated with the higher current will remain much more significant than that from normal pixels - ie you may need a lot more darks than conventional wisdom would suggest to get rid of all the dark noise. One possible solution - haven't tried it yet- may be to use aggressive and identical hot pixel replacement to get rid of the worst dark noise sources in lights, darks and flats before doing any other processing. EDIT: as Barry suggests.

Mike - yep, your technique is very powerful - and it is statistically likely that you are not a pariah.

the model is now at the stage where I can probably expand it to include dark and bias. Might be interesting to try, but will probably be quite a job due to the odd statistics of dark noise. Do you think it could be worth publishing the model? - it clearly is not of major importance, but the results are quite interesting and I haven't seen anything like it elsewhere. if so, any ideas where?

Last edited by Shiraz; 21-07-2014 at 03:34 PM.
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