Processing this image was a bit of a train wreck and I really only saw it through out of morbid curiosity to see if there would be any survivors. At first sight, my RGB image looked very promising with nearly 3 hours of integration time and an SNR level that was entirely manageable. That is until I subtracted out the background which left the image a noisy mess thanks to the copious amount of light pollution in my fair city. Honestly, I probably need twice the integration time to get enough SNR to write home about. A week later I spent another night acquiring about 2 hours of Ha data, hoping to boost my shotty SNR and enhance the fine nebular details with my first HaRGB project. But alas, the next morning I realized that all of the Ha sub frames were slightly out of focus. Rats! There goes my fine detail out the window. So what I need is at least two nights to capture more RGB data and to reshoot the Ha frames. With about two cloud-free moonless nights per year in coastal Virginia, I’d have this project complete by 2020.

Now, a wise man once told me that you cannot polish a turd. Yet despite the wisdom of these words I pulled out my shammy cloth and turtle wax to see if there was a gem somewhere in this mess. Since there was no chance of the poorly focused Ha image contributing to any fine detail, I used a synthetic LRGB approach were I created a synthetic luminance frame from the noisy RGB image and worked hard on noise reduction. This would be my detail. For the color contribution, I combined the Ha and RGB images using the PixInsight narrowband combination script to boost the reds. Usually with LRGB images the color is blurred or binned anyway, so having the Ha slightly out of focus wasn’t a deal breaker. To be sure, I tightened the Ha image up with deconvolution prior to narrowband combination. After applying non-linear stretches to both images using masked stretch and attacking the chrominance noise in the (HaR)GB image, I combined the two with LRGB combination. Standard post-processing techniques followed.

In the end, there are some good points to the image. I like the hue of the nebulosity and the noise level is about right. I certainly stretched the nebulosity as far as I could given the quality of the underlying data, but I like the Pelican kind of dim because it makes him extra spooky like a scene from Beetlejuice. And there are bad points too. The brightest stars were overexposed in the linear image and are afflicted with multiple reflections from my optical system so they look particularly unnatural. The concentric rings and odd colors around these stars certainly mark room for improvement as does the residual light pollution gradient in the upper left corner that I could not remove entirely. For composition, I would have framed the image down and to the left a little bit if I could do it again. There were two stars at the tip of the Pelican’s beak that “really tied the room together” as The Dude would say, but they were cropped out due to a misalignment of the RGB and Ha images. Oh well. C'est la vie. So will I do it again? Yes! But probably not until next summer - and next time I will boldly try a mosaic to capture the entire body of the Pelican. So much to do...so little clear sky…

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Processing Workflow (PixInsight)

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Pre-processing

1. Calibrate Ha and RGB light frames using darks, bias, flats

2. Integrate RGB light frames using Windsorized Sigma rejection method (ImageIntegration)

3. Drizzle integrate red channel of Ha light frames using sigma rejection method (ImageIntegration)

Post-processing

RGB image

1. Crop image to remove dithering borders (DynamicCrop).

2. Remove gradients (AutomaticBackgroundExtraction; subtraction).

3. Neutralize background (BackgroundNeutralization).

4. White balance (ColorCalibration using stars as reference).

5. Set luminance coefficients to 0.333333 for RGB channels (RGBWorkingSpace).

Ha image

1. Crop image to remove dithering borders (DynamicCrop).

2. Sharpen (Deconvolution;w/luminance mask)

Synthetic luminance

1. Extract luminance (ChannelExtraction).

2. Noise reduction (MultiscaleLinearTransform; w/linear mask).

3. Remove dark pixels (RemoveDarkPixel PixelMath process [Schwarz]).

4. Remove dark pixels (RemoveHotPixel PixelMath process [Schwarz]).

5. Non-linear stretch (MaskedStretch process; target background 0.2, iterations 1000).

Combined narrowband & RGB image

1. Combine narrowband and RGB images (NBRGBCombination script; RGB, Ha bandwidths 62.4 nm and 7 nm, scale 1.5).

2. Set luminance coefficients to 0.333333 for RGB channels (RGBWorkingSpace).

3. Noise reduction (MultiscaleLinearTransform; w/linear mask).

4. Non-linear stretch (MaskedStretch script; target background 0.2, iterations 40).

5. Reduce luminance noise (ACDNR; w/ luminance mask).

6. Combine synthetic luminance and boost saturation (LRGBCombination).

7. Reduce chrominance noise (ACDNR; w/ luminance mask).

8. Reduce greens (SCNR).

9. Compress dynamic range of large scale structures (HDRMultiscaleTransform; w/star mask).

10. Apply contrast curve (CurvesTransformation).

11. More stretching (HistogramTransformation; w/mask protecting stars).

12. Reduce luminance noise (ACDNR; w/inverse luminance mask).

13. Reduce chrominance (ACDNR; w/strong inverse luminance mask).

14. Reduce star sizes (MorphologicalTransformation, morphological selection; w/ mask selecting stars).

15. Sharpen star cores (MultiscaleMedianTransform; w/mask selecting stars).

16. Sharpen everything else (MultiscaleMedianTransform; w/star mask).

17. Remove dark pixels (RemoveDarkPixel PixelMath process [Schwarz]).

18. Balance over all colors by shifting the blackpoint of the red and green channels so background peak of histograms overlap (HistogramTranformation; red and green channels).

19. Lower background value to a little above 0.1 with contrast curve (CurvesTransformation).

20. Set ICC profile to sRGB for web publishing (ICCProfileTransformation).

References:

Manfred Schwarz galaxy tutorial:

http://www.astrophoto.at/PixInsight/