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Now that imaging season has come to an end, I have the time to do some reprocessing of old data, and I thought I'd share some of my processing methods.

I gathered much of that data before I started doing dithering, and the usual background I am fighting looks like this:

average_nocc_sc.png

This is an extreme crop of a wide field image of the area around NGC 1499 (the whole frame covers the area from M45 to Mirfak), taken with my unmodified DSLR. The stars show trailing, but this is not a problem in the final image.

Process details:

12 subs, integrated using AVERAGE integration with sigma clipping; no other method for hot pixel removal was used.

As you can see, despite pixel rejection during stacking, there is a streaking pattern. This is due to hot pixels which weren't removed during the calibration/stacking process.

In my case, it never made a difference if I used darks or not, some of this pattern always remained.

The next image shows the stacking result for the same subs, but this time with MEDIAN integration with aggressive percentile clipping (20 % high values) and hot pixel removal during calibration (cosmetic correction using the Auto Detect option in PixInsight):

median_cc_pc.png

As you can see, the streaks are gone, and it will be much easier to process this image further.

Since I can't control my cameras temperature, and it's difficult to match dark frames to light frames, I don't take dark frames anymore. Instead I rely on bias frames and cosmetic correction to remove read noise patterns and hot pixels.

During this past season, I started using dithering, and found this to be the best way to remove stacking artefacts and large scale noise (what Tony Hallas calls "mottle").

I typically dither 15 pixels between frames in a spiraling pattern. After star alignment, the dithering pattern looks like this:

M45_dithered.mp4

This is a crop of debayerd and aligned raw files. No calibration process or cosmetic correction was used.

As you can see, the stars are aligned, but the hot pixels move around and will be effectively removed during image integration.

If these were calibrated files, I would also have used cosmetic correction, and the number of hot pixels would have been much lower.

I hope this information can be of use to anyone fighting the same problems.

 

Cheers,

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It's possible you may need more than 12 subs for Sigma clipping.

I did use the standard cosmetic in DSS and did'nt ever see hot pixels.

Dithering is the number one weapon to clear a lot of the dslr rubbish, I now swear by it.
After seeing Tony's video I went out and bought a Lacerta MGEN and glad I did.
The stuff dithering allows me to see hidden in the rubbish is incredible.

I don't use DSS as such now, I use Tony's method with no darks flats or bias.
Intial RAW processing in ACR, convert to tif, register and combine in Registar.
Pixinsight to kill gradients etc and finish in PS
 

 

 

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Mike,

I agree, 12 subs is probably too few for sigma clipping to work properly. But the value at which one would clip, rather than the method for clipping should have more effect, I think.

What is more important, for a smaller number of subs the integration method (median vs average) should probably be median. For small samples, the average is more sensitive to outliers than the median value. Any outlier pixel values not dealt with by clipping will affect an average more than a median.

Taking care at the data gathering stage is always better than trying to fix the data in processing. But in the case no dithering was used during the imaging stage, and the image background shows streaks from hot (or luke warm) pixels, at least there is a straightforward method to reduce their  effect.

If I recall correctly, Tony Hallas recommends dithering (12 pixels at least) to get rid of what he calls mottle, large scale chromatic (?) noise. This differs from the streaks that were in my first image. But dithering takes care of both. And as you wrote: it allows you to see the faint stuff.

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