wimvb

An obsy. I'll post pictures later. Hopefully I'll be able to catch a few galaxies around Andromeda from it soon.

Starnet++ is your friend for this job. It's available as a standalone program as well as a script for PixInsight. Just google it. Process one copy of the image for stars, and one for maximum impact for the nebula. Remove stars in the latter and replace with those of the former. Btw, you got more colour variation in the nebula. It looks better, imo.

Thst's s very nice image of an unusual object. As the astrophotographer Metsavainio wrote, it's reminiscent of a wide screen version of the Rosetta nebula. I just wonder what happened to the small stars in your image? The star field reminds me of too aggressive star reduction in Pixinsight. But you used Gimp.

Excellent image, Rodd. Great contrast and brilliant control of the dark dust lane on the right. At the same time you kept the stars under control. 👍👍👍

Very nice, Dean. Did you have a lot of moisture in the air, or high cloud maybe? The image seems very soft, as if imaged through thin cloud. Btw, the tif file you posted, is unavailable.

That makes more sense. The shape threw me off, thinking it was a galaxy. But a PN is much more likely. Does anyone have a 3+ m fl telescope to zoom in on that small beauty? 😁

Excellent image, Rodd. Btw, you probably noticed it yourself, there is a small galaxy left of the nebula. I never noticed it before in images of this target. Usually it is just outside the frame.

The last one looks a lot better. Have you tried processing without trying to keep the ifn? Otherwise, assuming you use pixinsight, try multiscale hdr transform on the galaxies only. This will take their brightness down.

@AndrewG, I took the liberty of downloading your original image and put it through PixInsight. I hope you don't mind. I darkened it a bit and then applied a range mask to target the bright nebula on the left hand side, in order to put a bit more contrast into it. I applied Local Histogram Equalisation at about 25% strength, with scale factors 64 and 150. I then eroded the stars slightly with Morphological Transformation and a ring mask.

That made a difference. You pulled out a lot more of the faint nebulosity, but unfortunately introduced dark rings around the stars and purple colour burn on the lower right hand side. Image processing is a dark art. 🧙‍♀️

Better. You have more clarity in the bright nebula, but still good star control. Just for kicks, use starnet in PI to create a starless image. Then go all in on colour saturation and contrast. It probably won't give you an image you'd want to publish, but it will show you how much is in the data. Imo, you can't push data to its limit if you never cross that limit.

A very nice comeback. I think you can safely increase contrast to give it more punch.

Excellent, Göran. What? You can't do the processing in your sleep? 😉

That's why I always do gradient removal and background neutralisation as two separate steps. In pi there are several methods to remove a colour cast. Dbe which removes a gradient and can remove a colour cast Linear fit that can "align" median pixel values between monochromatic images before combining them Background neutralization that will shift and linearly stretch (a*x +b) pixel values based on a reference. Colour calibration, used to create a white balance after background neutralization Photometric colour calibration, similar to colour calibration All methods have their own algorithm. Unfortunately I know too little about their differences to go into further detail. @vlaiv what you describe in your post seems to me more or less what dbe does regarding gradient removal, and what background neutralization does regarding colour cast removal.

According to the link in my previous reply, it can. I haven't checked yet if there is an issue with that script.

Geralds method changed all fits header entries, it seems. Better test on copies of your images first.

In pixinsight the normal procedure is to first combine the channels and then do gradient removal, but you can do gradient removal first, of course. Gradient removal can include normalisation, which restores the median pixel value of the entire image. This leaves any colour cast intact. If you do not choose this option, gradient removal will also change the colour balance. And there is also the linear fit function, mentioned above. The idea behind this procedure is to have a common median or average pixel value for each master (Ha, S, O), in order to avoid a colour cast.

Wonderful indeed, and with lots of small galaxies in the background. Olly, please send some clear skies north, so I can have a go at this target. It seems to be slipping past me behind a solid cloud cover.

Sorry, missed your post. Is this what you're looking for? https://pixinsight.com/forum/index.php?topic=9721.0

No matter how you blend the channels, once you've removed the background gradient (without normalize in PI) and/or do a background neutralisation, your original blend is gone. On the very few occasions that I've processed bicolour, I used a mix that just looked good.

Don't get hung up on the numbers too much. The best (most detailed) images of dsos I've seen were taken with oversampled setups having an imaging resolution of 0.5 "/pixel or thereabout. I don't think that guiding error was half that value for those setups. Star blurryness depends on many things, and in Western/Northern Europe seeing is most likely the main culprit. As long as guiding error is 'considerably less' than any other cause, there's really no need to worry. To evaluate the tracking quality of your mount, you should analyse the guide graph and look for things like short period oscillations, backlash, etc, as well as possible differential flexure. If a guide graph doesn't show unexpected events, and images look good, the rig is very likely purring along nicely. @George Gearless if you used StellarMate defaults, you probably used the internal guider. StellarMate does have phd2 as an alternative. While the internal guider is simpler to use, phd2 is more powerfull and has tools to help you optimise your setup. Otoh, if it ain't broke, don't try to fix it.

That would be an excellent result for the mount. Normally you measure the rms ("average") error over a certain time period. How good your guiding really is, also depends on your imaging resolution (arcsecs/pixel). Multiply imaging pixelsize in micrometer by 206 and divide by focal length in millimeter to get imaging resolution. Your guiding error should be less than that value.

Looks very good. You got more colour variation in the veil. How did you combine the Ha data with the RGB?

Great image, Carole. I think I like this version best. You could even go for a tighter crop, removing the bottom bright stars, and some of the left and top. Imo, the data can take it.

Very nice image despite your troubles. As for a star mask to single out the big one, try this. In pixinsight use the cursor/pixel selector to determine the centre coordinates of the star. These will show at the bottom of the pi window. Then move to the right edge of the star. Note the change in x coordinate and determine the difference with the centre x coordinate. This will become your radius Then use pixelmath: Inellipse(x_c, y_c, radius, radius). Create a new mask image with the same width and height as that of your image, but gray scale, not colour. You may need to redo this a few times to get good coverage of the star. Finally use convolution to blur the mask. You can combine this mask with an ordinary star mask if you want. Again pixelmath: Max(circle_mask, star_mask) applied to either the circle mask or the star mask. Good luck