During an imaging run, you will see that you have really good images, average images and poor images.
Take a look at the following two images. Which one is better?
I say the one on the right is better. You can make out more of the nebula, which probably means the sky was more transparent. However, the left one is pretty good as well and I want to use it in the final image.
But doing this manually for hundreds of images is not fun, which is why we use PixInsight Subframe Selector
To grade our frames, we run the PixInsight Subframe Selector.
Before we can measure, we need to fill in the system parameters.
The easiest way to gather these system parameters is to simply upload one of the frames to Astrometry.net. Once processed and solved, Astrometry.net will provide the subframe scale in arcseconds/pixel.
In this particular example, the subframe scale is 0.938 arcseconds/pixel.
An alternative method is to use the formula
Arcseconds/Pixel = (206.2648 * (Camera_Pixel_Size_in_μm) / (Telescope_Focal_Length_in_mm)
Once done, execute the process.
Once each subframe is measured, we get a graph of the results.
The big question is what criteria should we use to determine a good/bad subframe?
- FWHM: Do we want to base it on how narrow the stars are, which might mean that these images are in better focus?
- Eccentricity: Do we want to base it on how circular the stars are, which might mean that the telescope was tracking really well for the subframe
- Stars: Do we want to base it on how many stars were detected, which might help us determine the transparency of the sky
You can change the measurement to see how each of the categories affects the graph. But in the end, you need to figure out what parameter(s) to use, and if you use more than one, how will each one get weighted.
Some sample formulas
(40*((FWHM-FWHMMin))/(FWHMMax-FWHMMin)) + (10*(1-(Eccentricity - EccentricityMin)) / (EccentricityMax-EccentricityMin)) + (10*((Median - MedianMin) / (MedianMax-MedianMin))) + 40
Using the (Param-ParamMin) / (ParamMax-ParamMin) gives a percentage for the particular parameter. For some parameters, like Eccentricity, a lower value is better, which is why the formal for that section begins with a “1-“.
Each parameter also includes a weight, allowing us to put more emphasis on particular parameters. The weights equals 60, which requires us to end the formula with a +40 to give us a final total of 100.
This formula works pretty well, but I find the weight on FWHM to be too overpowering. Also, it doesn’t take into account the number of stars, which helps determine how transparent the sky is. I believe more stars = better transparency.
This next formula is used by David Ault in his PixInsight tutorial.
(15*(1-(FWHM-FWHMMin)/(FWHMMax-FWHMMin)) + 15*(1-(Eccentricity-EccentricityMin)/(EccentricityMax-EccentricityMin)) + 20*(SNRWeight-SNRWeightMin)/(SNRWeightMax-SNRWeightMin))+50
I originally used this formula quite extensively. But I noticed that using SNRWeight doesn’t work extremely well. If the transparency is poor, then I might get more signal in the background sky fog due to the high light pollution I deal with. So subframes with worse seeing and more light pollution will come out higher than subframes with less light pollution.
This next formula is what I’ve been using.
(15*(1-(FWHM-FWHMMin)/(FWHMMax-FWHMMin)) + 20*(1-(Eccentricity-EccentricityMin)/(EccentricityMax-EccentricityMin)) + 15*(Stars-StarsMin)/(StarsMax-StarsMin))+50
Formula 2 was a big help in my formula 3. The main difference is that I substituted SNR with Stars. This is because of the light pollution in my area. A high SNR ratio doesn’t necessarily mean a good image. It could simply be that there is a lot of pollution in the sky reflecting all of the urban lights.
But in the end, you will need to figure out what approach works best based on your conditions and customize your own formula.
After entering in the formula, I also like to have the system automatically reject poor images. Most of the time, I’ve dtermined anything above a weight of 70 is good to include. Below I typically reject. By setting the approval parameter to
I can automate the pixel rejection process.
Identify Best Frames
It is a good idea to identify the best frame and the top 10% of your frames. We will use this information as part of the Star Alignment and the Local Normalization process.
Once we determine the subframe weights and reject poor subframes, it is time to output our new subframe inventory with a new weight parameter that we will using in the image integration process.
We do the following
- Change the routine to Output Subframes
- Select a new output directory
- Define a keyword which will store the subframe weight
The next step is to take take our best image, as our reference frame, and align all other frames with PixInsight Star Alignment.