More Thoughts on Drizzling

Thinking about the effects of combining SubFrameSelector approved frames (versus using all the eye-balled frames), and the effect of drizzling. I was looking specifically at the integrated image noise. However, maybe there would be a separate metric indicating the Signal to Noise ratio. Perhaps the noise is greater in the “better” frames, but the overall sharpness or contrast or something of the image is better.

So, I wandered through PixInsight and pulled in some more metrics as shown in the table below.

The column N indicates the number of subframes combined to make this image. For example, in the Green subs I combined 7 subs approved by SubFrameSelector for the first image. I combined all 9 available (eye-balled OK) frames for the second image.

Script FWHMEccentricity provided the columns Median Eccentricity,

Script NoiseEvaluation provided Noise StdDev (shows up on the console).

SubFrameSelector provided FWHM, SNRWeight, Noise. When images are drizzled, I divided the resulting FWHM by 2 for comparison.

Statistics tool provided MAD.

ContrastBackgroundNoiseRatio tool provided the CBNR value, which I think is fairly analogous to SNR.

A PixInsight forum discussion indicated that the ratio of avgDev to Noise would result in a SNR analog. I used MAD instead of avgDev since I happened to have those values, and MAD seemed to follow the pattern as avgDev.

ImageIntegration Effects
N Count MAD FWHM Median Eccent CBNR Noise StdDev SNR=MAD /Noise SNR Weight Noise
Green
SubFrames 7 0.002620 1.221 0.5315 0.19 9.45E-04 2.77 34.44 61.91
All Frames 9 0.002459 1.243 0.5422 0.26 8.44E-04 2.91 36.84 55.32
SubFrames & Drizzle 7 0.002304 1.36 0.4657 4.50 5.88E-04 3.92 56.23 38.51
All Frames & Drizzle 9 0.002603 1.3945 0.4893 5.12 6.22E-04 4.18 66.71 40.85
SubFrames & Drizzle4 9 0.002300 1.326 0.4771 5.50 3.42E-04 6.72 172.7 22.44
Red
 SubFrames 9 0.004080 1.182 0.5098 0.25 9.91E-04 4.12 45.97 64.93
 All Frames 10 0.004120 1.19 0.519 0.31 9.55E-04 4.32 48.58 62.58
 SubFrames & Drizzle 9 0.003550 1.299 0.4212 6.72 5.87E-04 6.04 83.94 38.49
 All Frames & Drizzle 10 0.003150 1.302 0.4363 7.36 4.99E-04 6.31 92.05 32.72
Halpha
 SubFrames 18 0.000649 1.252 0.5035 0.24 1.52E-04 4.26 39.05 9.977
 SubFrames & Drizzle 18 0.000524 1.3945 0.4352 6.97 7.94E-05 6.60 78.98 5.206
 SubFrames & Drizzle4 18 0.000527 1.35225 0.4446 7.66 4.61E-05 11.43 237.8 3.021
OIII
 SubFrames 11 0.001290 1.765 0.4085 0.44 4.68E-04 2.76 32.69 30.68
 All Frames 16 0.001201 1.775 0.3931 0.21 4.19E-04 2.87 37.7 27.43
 SubFrames & Drizzle 11 0.001220 1.847 0.3936 6.18 3.08E-04 3.96 61.19 20.2
 All Frames & Drizzle 16 0.001060 1.8765 0.3656 6.66 2.57E-04 4.12 71.67 16.85
UHC
 SubFrames 22 0.004610 2.004 0.402 0.28 6.09E-04 7.57 115.3 39.9
 All Frames 28 0.004426 2.05 0.3505 0.41 5.84E-04 7.58 114 38.28
 SubFrames & Drizzle 22 0.003934 2.1115 0.4063 9.24 3.61E-04 10.91 211.1 23.63
 All Frames & Drizzle 28 0.003540 2.1205 0.3273 9.21 3.25E-04 10.89 212.2 21.3

Conclusions

  1. Drizzle really helps the SNR ratios. I suppose this shouldn’t be a big surprise – I imagine that is the point of the whole process.
  2. Looking at Drizzling by 4 instead of 2, the noise only changes slightly but the SNR goes up significantly.
  3. Drizzling really helps the eccentricity of the stars. PI forums indicate that ideally, stars should have about 0.44 or less for eccentricity. My raw frames tend to be about 0.5, but after drizzling the eccentricity drops to 0.42 or better! Somehow the stars are rounder.

I looked at the Eccentricity plots produced by the FWHMEccentricity tool. The eccentricity is distinctly better in the drizzled image.

 

Eccentricity map for non-drizzled image.

Eccentricity map for non-drizzled Green image.

Eccentricity map for same Green images drizzled.

Eccentricity map for same Green images drizzled.

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PixInsight SubFrameSelector and Drizzle

I was reading about the PixInsight tool SubFrameSelector, and wanted to see if it would be more useful for eliminating poor images from my work process. So, I decided to try with and without bad frames to see the effect. Two issues were in my head:

  1. Historically, I have simply eyeballed the images and removed obviously bad subframes. Trailed stars, guiding errors, etc. Perhaps a more rigorous approach would yield sharper results.
  2. I read the presentation Image integration techniques : Increasing Signal/Noise Ratio and outlier rejection with PixInsight by Jordi Gallego. Most of the discussion was typical and reasonable. However, at the end he showed a shocking example where he added an obviously bad subframe with guide errors, and got a better integrated image! Hmmm, maybe I shouldn’t be eliminating the bad frames!

I had some images awaiting processing. They are from an SBig ST2000XM through a Tak Sky90, with various filters. I haven’t finished taking all the images (missing Blue, the HAlpha needs to be redone). Also, I have been experimenting with using a UHC filter for my Luminance frames. I know I am not supposed to use UHC for astrophotography, although I don’t know why.

I used the expression

(FWHMSigma < 2) && (SNRWeightSigma > -2) && (EccentricitySigma < 2.5)

to approve subframes. I should note that I had already removed obviously bad frames, so this was rejecting frames that “looked” OK.

At the same time, I wanted to play with the Drizzle tool. The criteria for using this are

  1. Lots of frames, on the order of 20. My sub counts range from 7 to 28 (15 minute) subs, depending on which selection criteria I use. So, I will test the effect of drizzling on smaller sub counts.
  2. Undersampled frames. My setup gives 3.74 arcseconds per pixel, so I think I meet this criteria. The FWHM reported by FWHMEccentricity is less than 2 pixels.

I also want to see the effect of drizzling with a scale of 2 versus 4. If 2 works, maybe 4 would be better, other than the huge file size?

The table below summarizes the results. I either integrated the subs recommended by SubFrameSelector, or used all of the frames. In the HAlpha case SubFrameSelector accepted all 18 frames, so there isn’t a smaller subset case.

I used the MAD value reported by the Statistics tool as my measure of noise. I also looked at average deviation (Statistics) and the NoiseEvaluation script; these values showed the same patterns as the MAD results, so I didn’t put them in the table.

ImageIntegration Effects
SubFrameSelector and Drizzle
n (Number Frames) MAD
Green
SubFrames 7 0.002620
All Frames 9 0.002459
SubFrames & Drizzle 7 0.002304
All Frames & Drizzle 9 0.002603
SubFrames & Drizzle4 9 0.002300
Red
SubFrames 9 0.004080
All Frames 10 0.004120
SubFrames & Drizzle 9 0.003550
All Frames & Drizzle 10 0.003150
Halpha
SubFrames 18 0.000649
SubFrames & Drizzle 18 0.000524
SubFrames & Drizzle4 18 0.000527
OIII
SubFrames 11 0.001290
All Frames 16 0.001201
SubFrames & Drizzle 11 0.001220
All Frames & Drizzle 16 0.001060
UHC
SubFrames 22 0.004610
All Frames 28 0.004426
SubFrames & Drizzle 22 0.003934
All Frames & Drizzle 28 0.003540

Conclusions

1. Rejecting subframes seems to be counterproductive.

Adding the additional subframes, rather than excluding the SubFrameSelector rejects, generally gave a somewhat lower noise result. The exception was the Red filter, where adding the one rejected frame made the noise slightly higher.

2. Drizzling works well, even with small frame counts.

Even with only 7 subs, drizzling gives lower noise. The noise is reduced, and the image is clearly better visually. Many of the dark pixels are gone, and the stars are much rounder and softer. See the zoomed previews below.

Drizzling with a scale of 4 versus 2 did not give appreciable improvement, so I don’t need to deal with the huge files:)

 

7 Green subs, no drizzling.

7 Green subs, no drizzling.

7 Green subs with drizzle.

7 Green subs with drizzle.