My T Tauri image needs a lot of love to bring out the nebulosity surrounding it – my images have always been noisy and I’m not entirely sure how much of that is just how the sensor is and how much of it is the processing. Anyway – the nebulosity around T Tauri actually goes by the name of NGC 1555/Hind’s Variable Nebula. Since T Tauri varies in brightness, so does the the visibility of the nebulosity. I think if you wanted to be able to see the nebula vary in brightness with as long of an exposure as this you’d have to come back to image it after at least a season, if not a couple years.  The shorter term period of 2.81 days wouldn’t let you get nearly as deep of an image with your average scope, although you might be able to see changes in the bright part right next to the star. NGC 1555 is a reflection nebula – the light from T Tauri is enough to illuminate the nearby dust, but not enough to ionize it to the point of becoming an emission nebula. I really want to come back to this in a few years and see how it might change.

Noise Reduction

Next thing I need to do is reduce the noise in the RGB and Luminance images with TGVDenoise. The first few times I processed this data, I did all these steps after combining RGB + L, but while searching for information on how to use TGVDenoise with linear RGB images I saw a lot of people saying that RGB and Luminance should be processed separately as long as possible. Even with just the TGVDenoise step, I’ve seen that I’ve had to use slightly different settings for the RGB and Luminance image so I have high hopes that this version will turn out better than the first time(s).

The default TGVDenoise settings are way too aggressive for my images. Strength starts out at 5.0, and edge protection starts out at .002. The default of 2.0 for smoothness works pretty well though. I also increased the number of iterations to 350. Entering the values for this process can be done one of two ways – either by entering the full value in the left side box, or by entering the significant figures in the box on the left side of the slider and the exponent on the right. Kind of useful for the edge protection value, since that is apparently quite sensitive to changes in the value and is usually a pretty small value.

Since the image is still linear I used the RGB/K mode for both RGB and Luminance.

TGVDenoise settings for my RBG image

I didn’t use the Local Support because it wrecked the image – from what I’ve seen it’s supposed to use a clone of the image to help protect it, but in my image it just made rings of all the stars regardless of playing with the settings. Kind of looks like what happens with water drops on paper…

TGVDenoise with local support enabled

Here’s the before and after of the process applied to the RGB image:

And here’s the before and after with the luminance image:

For the luminance I had to reduce the strength from .9 down to .7 – if I had tried this process with RGB+L I would have probably had to stick with the lower value and not reduced as much noise as possible for the color.

Stretching Images

I decided to go with Masked Stretch this time for no other reason than I hadn’t used it before and saw it pop up often in the processing steps listed on several Reddit r/astrophotography posts. Usually I use the Histogram Transformation process and clip the histogram that way, but this is definitely easier. These are the default settings for Masked Stretch and they worked great on my luminance image. I did have to increase he target background to .150 for the RGB image, but everything else was good. The stretch makes the darkest parts of the background kind of noisy looking for the luminance image, but even playing around with the settings I didn’t see a result I liked better. Most of them cut out too much detail.

Masked Stretch settings

RGB post-stretch to non-linear

Luminance post-stretch to non-linear

Reducing star sizes

I tried to do the LRGB combination next, but the star sizes in the luminance image were so much bigger than in the  RGB image that it was making things look weird – the image was really washed out and seemed to lose a lot of the color. I followed a tutorial from Light Vortex Astronomy here on how to reduce star sizes. Basically you create a contour star mask and use Morphological Transformation to reduce the stars a bit. The contours should match the shape of the stars as closely as possible, and cover only stars, not background noise.

Settings I used for this star mask

Contour star mask

I used the same settings as the tutorial, except I had to reduce selection from .20 to .10 to reduce the stars by the amount I wanted – .20 wasn’t quite aggressive enough.

Settings I used to reduce the star sizes a bit

Before and after reducing the star sizes a bit on the luminance image

LRGB Combination

Combining the luminance with the RGB is pretty straightforward with the LRGBCombination process – set the L option to the view that contains your stretched luminance image, adjust the channel weights as needed, and drag the triangle to apply it to the view with the RGB image. Since I did my color calibration already (and this is only adding luminance anyway) I left the weights all as 1. I did reduce the value of the saturation transfer function to try to get a bit more color though (50/50 on whether that was a good idea or not).

LRGBCombination settings

Combined image

Reducing Bright Stars

The 3 brightest stars could still be a little smaller, especially T Tauri itself since it’s creeping up on the reflection nebula. To do this, create a range mask instead of a star mask and use HDRMultiscaleTransform on the image. The settings for the range mask will depend on your image – basically what I did was mess around with the upper and lower limit on RangeSelection until I got spots that corresponded to my brighter stars, and then increased the smoothness to 30 or 40 to get blurrier edges. I then repeated the RangeSelection process on the range mask itself (2 more times), tweaking the smoothness and the limits until it looked like this:

Range mask for reducing stars

The diffraction spikes made one of the stars kind of square but I think it still worked. Apply the mask to the image and apply these HDRMultiscaleTransformation settings:

It’s pretty subtle but it tightened up T Tauri and kept it from blurring into the adjacent nebula.

Before and after reducing the brightest stars

Local Histogram Equalization

The last thing I did was increase a tiny bit of the contrast with the LocalHistogramEqualization process. I used the range mask from above, but inverted to protect the star areas. I increased the kernel radius to give a better sampling of the image and reduce the contrast limit and amount way down to keep the result from creating too harsh of a boundary between the nebulosity and the background. This one is 50% of the new result and 50% of the original image, anything more than this and it was just too harsh.

Final result

 

At this point I’m having a hard time figuring if this image has been overprocessed, underprocessed, or if there was a better way to do this, so I’m calling this one done for now — constructive criticism pending. I’ll probably come back to it sooner than later since I have so much data on this pretty little star and I really want to see what it looks like when it’s at its best. One thing I really want to figure out is how to make this thing less splotchy and noisy – it seemed like the more I did to try to fix it the worse it got. There were actually other processes I tried but they either didn’t solve whichever problem I was trying to solve or made it worse. Another issue is the color – the RGB image was much redder than this, and that matches some of the other images I’ve seen of T Tauri. I’m not sure why the combination with the luminance image made it look like this. The red is still there since you can see it in some of the stars, but it’s not quite what I think it should be.

Bonus!

I was playing around with some of the scripts available in PixInsight and found this cool one called Annotate Image. You can pick from almost any useful catalog of objects (or add your own if needed) to find and label objects in your image. There’s an option to remove duplicates, so if you have them, make sure to sort the catalogs used in your annotation to match your own order of importance – I had to move the GCVS catalog above the TYCHO-2 catalog so T Tauri would be labeled T Tau and not some TYC-blah-blah name.

T Tauri with the fun bits labeled