Many thanks for uploading.
As surmised, in this case too, the thing holding you back most is failing to dithering between frames - clear, correlated pattern noise and hot-pixel steaks can be seen in the vertical direction. If you go for any fainter objects, dithering is not really optional with most instruments. For DSOs, flats are certainly not optional.
Your dataset will also improve by choosing a different (outlier) rejection algorithm when stacking. At the very least, it will get rid of the satellite trail. Using a stacker that allows you to turn white balancing off (e.g. the latest versions of DSS) may also help give you a slight boost in signal fidelity.
The image you posted is not too bad considering the dataset. It is, however, oversampled, so you stand to gain by
binning it. This yields a better signal at a lower resolution, but without loss of detail. The better signal, in turn, can be used to process the image better (for example with deconvolution).
This sort of advice (and even examples that show issues similar to yours) can be found in the
section I linked to earlier, so definitely have a read.
Finally, see if you can improve your tracking - your stars are elongated, and any detail is similarly smeared out in the vertical direction making your image a lot softer and blurrier than need be. Deconvolution can also restore some of that detail, but only if the signal is clean and good.
In general, you will find that you need to spend a lot less time in post-processing or working around easily avoidable issues, if those issues don't exist in the first place!
I tend to try to avoid giving workflows that focus mostly about working around dataset issues; you don't really learn anything from them you can use going forward. So below is a workflow that is as simple/standard as possible given the limits of the dataset;
--- Auto Develop
To see what we got. Some issues noted above.
--- Crop
Better framing of the pair.
Parameter [X1] set to [775 pixels]
Parameter [Y1] set to [251 pixels]
Parameter [X2] set to [2010 pixels (-1086)]
Parameter [Y2] set to [1179 pixels (-901)]
--- Bin
To convert oversampling into better signal.
Parameter [Scale] set to [(scale/noise reduction 50.00%)/(400.00%)/(+2.00 bits)]
Image size is 617 x 464
--- Wipe
Parameter [Dark Anomaly Filter] set to [5 pixels]
--- Auto Develop
RoI over slice of the pair.
Parameter [Ignore Fine Detail <] set to [6.2 pixels]
Parameter [RoI X1] set to [192 pixels]
Parameter [RoI Y1] set to [222 pixels]
Parameter [RoI X2] set to [397 pixels (-220)]
Parameter [RoI Y2] set to [291 pixels (-173)]
--- HDR
Default (Reveal All)
--- Deconvolution
Auto-generate mask.
Attempt to counter some of the smearing out of detail due to bad tracking;
Selected (click) a not-masked-out star as secondary PSF. Now Decon has an idea of how a point light (star) is smeared out.
Parameter [Secondary PSF] set to [Dynamic Star Sample Small x Primary]
Parameter [Primary PSF] set to [Moffat Beta=4.765 (Trujillo)]
Parameter [Tracking Propagation] set to [During Regularization (Quality)]
Parameter [Primary Radius] set to [4.5 pixels]
Parameter [Iterations] set to [21]
--- Color
Default color balance is too green, this is a common problem when a stacker does not align channels properly (either to due to the stacker's effectiveness or some sort of chromatic aberration in the optical train). Indeed you can see stars that have red edges at the top, but blue edges at the bottom.
Flip the Color module into MaxRGB mode, so you can see aberrant green dominance (green dominance is rare -
see here on more information about how to color balance in MaxRGB mode).
Parameter [Green Bias Reduce] set to [1.47]
Parameter [Cap Green] set to [100 %]
--- Wavelet De-Noise (turn Tracking off, choose grain removal)
Parameter [Grain Size] set to [8.6 pixels]
--- Wavelet De-Noise
Default.
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- Stack_32bits_33frames_1980s.jpg (32.21 KiB) Viewed 6621 times
Hope this helps!