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Last night I did something that felt completely wrong. I threw away one-fifth of my hard-earned astrophotography data. Hours of imaging time, just… deleted. And you know what happened? My image got sharper. Noticeably sharper.
Look, I know how this sounds. We’re told constantly that more data equals better images. Stack everything. Every photon counts. But here’s the truth I’ve learned the hard way: bad data doesn’t just fail to help your image – it actively makes it worse.
Definition: Frame selection is the process of removing poor-quality sub-exposures before stacking to improve final image sharpness.
Watch my video:
The left side of my comparison showed all my data stacked together. The right side? Only the best 80%. The difference was immediately obvious – tighter stars, crisper detail, better contrast. It was like someone had cleaned my telescope’s optics, except all I’d done was be more selective about which frames I kept.
Today I’m going to show you exactly why you need to start deleting your own files, and more importantly, how to do it automatically in Siril so you’re not sitting there for hours squinting at individual frames. Let’s dive in.
What is FWHM in Astrophotography?
I know, I know – another acronym. But stick with me because this one is actually dead simple and incredibly useful.
FWHM stands for Full Width at Half Maximum. Sounds complicated, right? It’s not. It’s just a fancy way of measuring how fat your stars are.
Imagine looking at a single star from the side. In a perfect world, that star would be a tiny pinprick of light. But because of our atmosphere and our optics, it spreads out into a hill shape – like a bell curve. To measure how sharp that star is, we go halfway down the hill (half maximum intensity) and measure how wide it is at that point.
That width, measured in pixels, is your FWHM number.
- FWHM of 2-3 pixels? Your hill is skinny. Your star is razor sharp. You’re winning.
- FWHM of 6-7 pixels? Your hill is a fat blob. Your star is blurry. Something went wrong.
Lower is always better. That’s all you need to remember.
This FWHM number is brilliant because it gives us an objective way to check our focus, assess how good the seeing is, and most importantly for today’s discussion – figure out which frames are worth keeping and which ones are dragging down our final image.
I’ve been recommending for ages that you filter your frames by FWHM, though roundness is another good metric (we’ll get to that in a minute).
What’s Actually Ruining Your Frames?
Before we get into the solution, we need to understand the problem. Why is one frame gorgeous and the next one garbage? It usually comes down to three culprits:
1. Seeing
The atmosphere is constantly moving, with pockets of warm and cool air swirling around. Looking through it is like trying to see clearly through water. Some moments the air is calm and your stars are tight. Other moments it’s turbulent and your stars turn into bloated messes. This is completely out of your control, which is frustrating, but at least we can measure it and work around it.
2. Wind
Even a gentle breeze can shake your rig. I’ve learned this the hard way countless times. You think everything is solid, but then you look at your frames and half of them have slightly elongated stars because a gust of wind hit during the exposure. If you’re using a long focal length like I often am, even tiny vibrations show up.
3. Tracking Errors
Unless you’ve spent thousands on a top-tier mount (and let’s be honest, most of us haven’t), your tracking has errors. The gears aren’t perfect. They speed up and slow down slightly as they turn. Some frames get captured during the smooth parts of the tracking cycle. Others get captured during the rough patches. It’s just the reality of working with affordable equipment.
Why Bad Frames Poison Your Stack
Here’s the bit that took me ages to really understand: when stacking software combines your images, it doesn’t intelligently separate the good from the bad. It just averages everything together.
If you mix 50 crisp images with 10 blurry ones, the maths doesn’t ignore the blurry ones. It drags the average quality down. It dilutes your sharpness.
Think of your telescope like a pen. On a good night with stable seeing and calm winds, it draws sharp dots. But on a windy night or when the seeing is rubbish, your telescope turns into a spray can. The light goes everywhere.
If you stack a spray can frame on top of a fine pen frame, you don’t keep the sharpness of the good frame – you ruin it. You blur it out. The solution is simple but psychologically difficult: we need to find those spray can frames and delete them.
Astrophotography Frame Selection
How I Used to Do This (The Slow Way)
For years, I’ve been going through my images manually. One by one. Zooming in, looking at the stars, deciding whether each frame looked sharp or soft. Then deleting the bad ones.
This works, sort of. But it has problems:
- It relies entirely on my eye and my judgment. Where exactly do I draw the line?
- It’s incredibly slow and boring, especially when I’m using short exposures and have hundreds of frames
- I’m not consistent. Sometimes I’m tired and I’m more lenient. Sometimes I’m fresh and I’m ruthless.
- It’s not scientific. I’m guessing.
I kept thinking there had to be a better way. Something more objective. Something faster.
The Automated Way (That Actually Works)
I’ve now found a method using Siril – the free software I love and use constantly – that does this automatically. It’s faster, it’s scientific, and honestly, it works better than my eyeball method ever did.
Before we get into the how-to, do me a favour. Pause reading for a second. Open your last session in Siril (or whatever software you use). Go to the plot tab. I bet you – I absolutely guarantee you – there’s a massive spike in your FWHM graph that you didn’t notice when you stacked everything. That one spike is actively making your final image softer.
Go look. See if I’m right. Then come back.
The Step-by-Step Process to Cull Images in Siril
Alright, let’s automate this properly. If you haven’t already, go and download the free software Siril from their website.
Step 1: Load your sequence in Siril and go to the plot tab
This is your lie detector. This is where the truth lives.
Look at the FWHM graph. See how it’s mostly flat, but then it jumps up in places? Those spikes are where the seeing got bad, or the wind picked up, or your mount had a hiccup. We don’t want those frames.
Step 2: Go to the stacking tab
Don’t just hit stack like you normally would. Look at the filtering options. This is where the magic happens.
You can tell Siril: “Only keep frames where FWHM is below 4.0” or “below 5.28” or whatever number makes sense for your data.
Even easier, you can tell it: “Keep the best 85% of images based on FWHM.” Siril will automatically uncheck the worst 15%.
Boom. The spray can frames are gone.
Step 3: But wait – there’s a second killer
Sharpness isn’t the only thing that matters. You can have a very sharp frame that has trailing stars because your mount kicked slightly. The stars are thin, but they’re ovals instead of circles.
FWHM might not catch this because the star is technically thin in one direction.
This is where eccentricity (also called roundness) comes in. It measures how circular your stars are. If you’re really chasing perfection, you can do a two-stage filter:
- First, delete the blurry frames using FWHM
- Second, delete the trailed frames using roundness
This ensures your stars are both tight and round. That’s the goal.
But Should You Always Delete Bad Frames?
Here’s where it gets nuanced. There’s a tradeoff between sharpness and noise.
If you’re shooting a very faint target and you only have one hour of data, deleting 20% of it might make your image way too noisy. You lose the smoothness of the background. The signal-to-noise ratio suffers.
But if you have 10 hours of data? You can afford to be ruthless. Deleting the worst hour won’t hurt your noise much, but it will massively help your sharpness.
The rule is simple: the more data you have, the pickier you should be.
I’ve started thinking about it like this – if I have less than 2 hours on a target, I’ll only delete the worst 10%. If I have 5+ hours, I’ll happily delete 20% or even more. It’s about finding the balance for your specific situation.
My Honest Take: Stop Being a Data Hoarder
Look, I get it. You spent hours outside in the cold capturing that data. The idea of deleting any of it feels wrong. It feels wasteful.
But here’s the thing: if a frame is genuinely bad, it’s not adding signal. It’s adding pollution. It’s making your image worse, not better.
Trust the maths. Let it go.
I’ve tested this now on multiple targets and the results are consistent. Filtering out the worst frames improves sharpness without any meaningful downside (as long as you have enough total integration time).
FAQ: Frame Selection in Astrophotography — What You Really Need to Know
Q: Do I really have to delete frames I spent hours capturing?
I get it — deleting your hard-earned data feels wrong. But here’s the thing: stacking blurry or bad frames with good ones doesn’t help. It just drags down the whole image. If you’ve got enough total exposure time, cutting out the worst 10-20% can make your stars pop and your details sharper. Trust me, it’s worth it.
Q: What’s a good FWHM number to aim for?
There’s no one-size-fits-all number. It depends on your gear and the night’s conditions. Instead, look for spikes in your FWHM graph — those are the frames where the atmosphere or wind messed things up. Keep the frames clustered around your “normal” FWHM and toss the outliers. I look at the plot generated in Siril and decide on a number there. How I do this is explained in my video here:
Q: Will deleting frames make my image noisy?
It can, if you’re too aggressive and don’t have much data. If you only have a short session, be gentle — maybe delete just the worst 5%. But if you’ve got hours of data, you can afford to be picky and delete more without losing smoothness. It’s all about balance.
Q: Should I focus on FWHM or star roundness?
Both matter. FWHM tells you how sharp your stars are, while roundness (eccentricity) shows if your mount or wind caused star trails. I usually filter by FWHM first, then check for any oddly shaped stars and remove those too.
Q: Can I fix blurry stars later in Photoshop?
You can try, but it’s tough. Sharpening tools work best on already sharp images. Starting with a clean, sharp stack makes post-processing way easier and your final image much better.
Try This Tonight
Here’s what I want you to do with your next stack:
- Stack it normally first, keeping everything
- Then stack it again, filtering out the worst 10-20% based on FWHM
- Compare the two results side by side
I think you’ll be surprised. I was.
The stars will be tighter. The detail will be crisper. The whole image will have more “pop” to it.
This is one of those techniques that seems counterintuitive but makes a massive difference once you actually try it. It’s changed how I process every single image now.
If you want to learn more about how I capture this data in the first place, check out my other articles on imaging techniques and equipment. I’ve also got guides on post-processing, choosing affordable gear, and dealing with common problems like light pollution and poor seeing.
And if you try this technique, let me know how it goes. I’m always learning and I’d love to hear about your results.
P.S. – If you found this helpful, you might also like my articles on stacking techniques and choosing the right equipment for astrophotography. I’m constantly testing new methods and sharing what actually works with affordable gear.
