[gregbradley]

Thanks Lewis.

I do similar.

A few points. Firstly data rejection should be done after registration and on each filter set not on the masters. Data rejection requires a certain number of subs (the more the merrier) for the statistical maths used to identifier what is noise and what is most likely signal.

Mean versus Median. Rick has said Mean gives best Signal to noise ratio so I checked. In some instances it gave a tiny bit more but often it was the same.When it was better it was negligible but it does not remove noise as well as median. Mean is another word for average. Median is the midpoint in a set of numbers like 3,5,7 the median is 5 and the average is also 5. But in some sets the median and mean will be a different number.
A set of data that has some high numbers that are noise may show up better with median than mean/average. Say 3, 5 ,100 the mean is 36 but the median is 5. So the noise does not skew the image as much and odd values get dropped out as 100 is a long way from 5 so it shows up as an outlier ( a value that is outside the usual value of a set and therefore most likely noise).

If you watch the subs when using mean you can see it get more solid but some noise artifacts, satellites etc may not disappear. Now use median and as long as you have enough subs to make a meaningful statistical sample those artifacts will vanish.

I am not sure how maximum and minimum combine work but I have found them to not be so useful.

As far as noise reduction goes I think its common practice in digital imaging to do it early in the process. I don't always but I have started to. It makes sense as a lot of the stretching or enhancing steps will boost the noise as well.

With my Trius 694 I check each new set to make sure what calibration works. Sometimes flats (with no bias subtracted), darks and biases or sometimes bias subtracted flats, darks. Sometimes best is only bias (it gets rid of the whitish left hand edge of the Trius 694 images. I found recently some bias subtraction was showing the fixed pattern noise of the sensor - a fine grid pattern. I started using sigma reject for my biases and flats. That helps. But CCDstack has memory issues so I can't open all my luminance files even with an 8 hour total LRGB image. Its limited to about 15 x Proline 16803 files (32.4mb each) or about 30-40 Trius files (11.4mb each) before I get memory problems. I might start using Pix Insight soon for this as Rick has pointed out it has no problem with that. Also it has drizzle and CCDstack does not.

Before I combine my subs for a particular filter I normalise them. I drag the box over to include a dark area and a bright area in the same box. You normalise to again to help identify the outliers so the noise stands out. Normalise means to make the range of values to be similar or the lowest number in each to be 1. I then run data rejection and hot pixel then interpolate then cold pixel then interpolate. I have seen others use the other data rejections but usually I have not found it to be that useful. Once data rejection is done then I median combine to make a sample. When doing registration I am now using Lanczos 256 to resample the data as bicubic sampling can slightly blur the image (very minor though). Lanczos takes ages though.


1. I open the subs for one filter in CCDstack. I flick through them and erase duds. Clouds, errors, bad tracking etc.
2. I calibrate them (I usually experiment to see what works best for the Trius but the Proline its darks, flats (no bias subtract, don't ask me why it just works better for my CDK), bias.
3. Normalise using a dark and bright area in the dragged box.
4. Data rejection hot pixels/interpolate, then cold pixels then interpolate (this gets rid of spots and if not done you may get colour smarties noise in your background later on in processing).
5. Register the images and resample using Lanczos 256.
6. Do a median combine and save as a 32 bit floating FITs. I may do deconvolution 25 iterations positive constraint on the luminance or a fat star RGB master to make the stars similarly sized.
7. Do the same for the rest of the filtered images.
8. Register them all using the Luminance as the base image. Lanczos 256 resampling and save them overwriting the earlier file as they are now aligned with each other.
9. Do an LRGB colour create.
10. Save as a 16bit TIFF once I am happy with it.
11. I sometimes save the masters as 16 bit scaled tiffs so they can be opened in Photoshop without any stretching. But if you do this in CCDstack you need to lighten the background and watch the histogram as it will default black clip your images.
12. Open in Photoshop and do a stretch/colour processing/noise reduction etc.

Greg.

Greg.