PixInsight IntegerResample Guide:
When to Bin Your Astrophotography Data
First published April 2024
Last updated February 2026
Intro
Binning is a topic that confuses a lot of astrophotographers, myself included! In simple terms, it involves combining pixels to trade resolution for improved signal and noise performance. In this guide, I focus on using PixInsight to help you decide whether binning your data makes sense, and when it can genuinely improve results rather than throwing away useful detail.
Massive disclaimer
Let me begin by making clear that I don’t fully understand binning! It’s a complex topic, and if you want to really comprehend it, then this guide isn’t for you. But, if you just want a top-level understanding, and to ultimately know whether or not you should bin your data, then hopefully this will do the trick.
I can’t promise that all my info is accurate, but the method I outline does work. Any useful info I impart is thanks to the good folks over on The Stargazers Lounge, who have taught me basically everything I know. If you’re not already a member, I recommend joining.
If you’d like to correct anything I get wrong in this guide, then please leave a comment at the end or contact me here.
A bit of background…
Ok, so you’ve heard about binning, but aren’t really sure what it’s all about. That’s understandable, it’s pretty confusing. Let’s give a brief overview.
Consider that there’s always a limiting factor that provides a ceiling to the quality of data you collect. You may assume this is the camera sensor, but it’s actually more likely to be your sky conditions (called “seeing”). Your telescope and mount are also factors. You could have an amazing astro camera, but if your’re imaging through a tornado, your data won’t be very good, right? That’s an extreme example, so here’s a more realistic one. You have a camera sensor with lots of pixels, and they’re small too (6248 x 4176, 3.76 μm pixels in the case of my ZWO ASI2600MC Pro). But do my turbulent city centre skies actually let me capture useful data with all of those pixels? Maybe, maybe not — but we can use PixInsight to find out.
Ultimately, what we want is to match our camera sensors to real-life imaging conditions in order to have an optimal setup. This doesn’t mean buying a whole new camera sensor, because we can change its specifications by binning. I’m going to run through two examples, and then will explain a bit more background.
Example 1: the Iris Nebula, Askar FRA400 and ZWO ASI2600MC Pro
Let’s work this through using an actual example. First, we want to analyse an image to get its vital statistics. Then we can work out whether we should bin. For this example we’ll analyse an image from my archives: The Iris Nebula, taken using my Askar FRA400 telescope and ZWO ASI2600MC Pro camera.
To analyse images using PixInsight, I like to use PSF Image Creator. It’s a free plugin — details here.
Once that’s installed, load up a freshly integrated image. Don’t do any processing to it (well, you can perform an STF if you like). Then go to Script -> Render -> PSFImage. Click Evaluate and wait for it to complete. Then you should see a view a bit like this:
I’ve highlighted what we’re interested in: the two FWHM values. Calculate the average (so about 2.22 in this case). Think of this as telling us how sharp the image is. (This is a simplification, but it works for us). Lower generally means sharper stars, but what matters most here is how this value relates to pixel sampling. We need to divide this number by 1.6 to estimate how much binning we can apply before reaching critical sampling. 2.22 / 1.6 = 1.39.
Then we follow this general rule: if the value is close to 1, don’t bin. If it’s closer to 2, bin x2. If it’s closer to 3, bin x3. If it’s closer to 4, bin x4. In this example my result, 1.39, is closest to 1. So, I’m better off not binning.
Example 2: the Cygnus Wall, Askar 130PHQ and ZWO ASI2600MC Pro
Let’s try another example, this time on a different target and with a different telescope: my Askar 130PHQ.
First we’ll run PSF image to ascertain the image resolution:
The two FWHM values are 2.62 and 3.25. Take the average (i.e. 2.94) and divide by 1.6 to get 1.84. This result is close to 2, indicating that bin x2 will move the data toward critical sampling.
Go to PROCESS -> All Processes -> IntegerResample. Select the correct view using the dropdown menu at the top (Ult_integrated in my example). Our Resample factor is 2 (because we’re binning x2). And we’re downsampling. Then click on the blue square at the bottom:
A warning message may pop up, but it’s fine to click Yes:
And that’s it! If you just follow those steps then you’ll know whether it’s optimal to bin your data. If you’ve got a bit more attention span left in you then we can discuss it further…
Undersampled or oversampled?
If you should bin but you don’t then you’re likely oversampled. This means you’re wasting resolution and missing out on the upside to binning (see below). If you’re on the other side — for example, if you bin when it’s not necessary — then you’ll be undersampled, and missing out on some fine detail and your stars may look blocky.
The downside to binning
A lot of people are put off binning because there’s a downside: your image’s resolution (in terms of how many pixels large it is) is reduced. Using my 2600MC Pro as an example:
| Native resolution | 6248 x 4176 |
| Binx2 | 3124 x 2088 |
| Binx3 | 2082 x 1392 |
| Binx4 | 1562 x 1044 |
We want lots of pixels, right? Lots of pixels means lots of detail? Well, not necessarily. Remember that our limiting factor is likely sky conditions, and perhaps mount quality. Taking example #2 (the Askar 130PHQ and 2600MC Pro), the data were significantly oversampled, meaning many pixels were recording atmospheric blur rather than real detail. It’s a waste. This is why is makes sense to bin — because of the upside to binning…
The upside to binning
We can trade our camera sensor’s pixels for a boost in signal to noise ratio. This is because when we bin, we’re combining pixels. Bin x2 turns four pixels into one really big pixel. This in turn reduces the noise level, which effectively boosts signal to noise ratio (SNR). Cuiv the Lazy Geek made a good video explaining this.
So, returning to example #2, if I’m wasting pixels on my blurry atmosphere, I may as well trade them in for a very welcome boost in SNR. This upside is really excellent, so if you calculate your two numbers and find you’re in the middle of whether it’s beneficial to bin or not, you probably should, in order to get the SNR boost.
Guiding
We want our guiding RMS to be as low as possible, but how do we know if it’s good enough? Here’s a good tip: you want your guiding RMS to be half or less than your working resolution. So, in example #1 we were operating at 1.39. 1.39 / 2 = 0.7″. So, if your guiding is 0.7″ or lower (and smooth, not jumping around) then all is well. If it’s higher then look to improve it by better telescope balancing, guidescope settings, mount maintenance, or if necessary buying a new mount.
What about example #2? 1.556 / 2 = 0.78″. Same rule applies; I want my mount’s guiding to be 0.78″ or lower.
Just out of interest, what if I didn’t bin that data? Natively it’s 0.778, so my mount would need to be guiding 0.4″ or lower. When I first wrote this article, my mount wasn’t able to consistently achieve this, so it’s another reason that binning made sense; not only were my sky conditions limiting me, but the mount too. Now I have a StellarDrive X 6R PRO that guides in the 0.3″ – 0.4″ range, meaning that I can actually not bin and the mount can handle that.
Upsample at the end
If you’ve used PixInsight to downsample your image, your very final step in processing can be to run IntegerResample again at the end, but this time upsampling by however much you originally downsampled. This will get you your pixels back so you can have nice big images for printing or cropping.
Makes sense?
There’s a lot to take on board there! Hopefully this guide has helped you. Just to reiterate that this is at the limit of my understanding, and I’ve likely gotten some things wrong. But the methods outlined do work for me. If you’d like to correct me on any points then please do give me a shout and if necessary I’ll update this guide.
Book a Masterclass
One-on-one online tuition.
Art Store
Own a fine art print from the gallery.
Donate
Help me fight the spam bots!
Discover more from Urban Astrophotography
Subscribe to get the latest posts sent to your email.
thank you!
Great material. Thanks for sharing 🙂
great info. Just wondering why you divided the two values for FWHM by 1.6?
Dividing by 1.6 gives you the optimal sampling rate. I obtained this information from some very clever people, and don’t pretend to understand it in any depth!
Excellent information Thank you. Does this negate binning in camera first ? or effectively are we doing the same thing but have the option and understanding of each images needs so may not need to do it based on these conditions.
Thanks Pele. It’s best to bin in software rather than in camera for a few reasons, such as alignment algorithms working better with unbinned data. However, binning in camera might trump that if harddrive space and upload speeds are particularly important considerations for you. I think it’s generally considered optimal to bin after pre-processing registration but before stacking, but I haven’t found an efficient way of doing this in PixInsight, so I just bin the freshly integrated stack and it seems to work ok!
Hi Lee, very nice clear explanation. One question, where does the 1.6 factor come for sampling?
**Update to my previous comment**
I checked the astronomy.tools website and they use a factor of between 2 and 1.49 (using Nyquist factor of 2 but changing it slightly to allow for round stars covering multiple pixels) so your 1.6 seems appropriate
Thank you for your post – I’ve been trying to find a way to quantify how well my EdgeHD 8 is performing and using the PSF Image Creator on my sub-exposures is a great way to do this. One note … the PSF Image script likely reports FWHM values in pixels rather than arc-seconds which alter your comparisons a bit. No need to know the pixel scale; if the adjusted (divided by 1.6) FWHM is near 1 don’t bin, if it is closer to 2, a 2×2 bin is the way to go, etc.
Hi Michael, thanks for pointing this out! You’re quite right. I’ll edit this article when I’ve got some time, to incorporate your advice.