Staring at the Sky: How I Stacked Images with Python and Didn't Go Completely Insane
Amit Sadaphal
January 18, 2026
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How I Stacked Images with Python and Didn't Go Completely Insane
Forget those crisp, perfect space images you see. Getting those is hard. Like, really hard. Deep-sky objects are dim, cameras aren't magic, and noise is a pain. The 'fix' everyone uses is stacking multiple exposures to improve the signal-to-noise ratio. I remember when I first tried this, I thought 'Oh, it's just averaging pixels.' I was so, so wrong.
My Balcony Telescope (aka: The Frankenstein Setup)
I'm using a "smart" telescope. Which, honestly, is just a camera with some software bolted on. No eyepiece, no looking. It takes pictures, and I have to deal with them. I collected about an hour of exposures of the Caldwell 19 (IC 5146) nebula. It sounds impressive, right? It mostly means my laptop was running all night and I was hoping the power wouldn't go out, again.
Speaking of my setup, forget those fancy equatorial mounts you see in the magazines. I don’t have one. The smart-telescope relies on software to compensate for the Earth's rotation. It gets the job done, but sometimes I swear it drifts more than my grandfather telling old stories.
Python, OpenCV, and a Whole Lot of Coffee
I use Python and OpenCV. Why? Because I already know them, and I’m too old to learn something new. Alignment is the big headache. OpenCV has some functions, but they're not perfect. I spent a solid day tweaking parameters until the stars mostly lined up. Pro tip: don't expect perfection. Good enough is good enough.
Bare bones Image Stacking (Don't copy/paste this)
1# Example of a simple image stacking function (simplified!)
2import cv2
3import numpy as np
4
5def stack_images(image_list):
6 # Ensure all images are the same size and type
7 first_image = image_list[0].astype(np.float32) #convert to float for processing
8 stacked_image = np.zeros_like(first_image)
9 for image in image_list:
10 stacked_image += image.astype(np.float32) #sum the images as floats
11
12 stacked_image /= len(image_list) #average by the number of images
13 return stacked_image.astype(np.uint8) #return to original 8-bit int datatype
14
15# Load your images (replace with your actual image loading code)
16#image_paths = ["image1.jpg", "image2.jpg", "image3.jpg"]
17#images = [cv2.imread(path) for path in image_paths]
18
19#stacked = stack_images(images)The Result (and Why It's Not on NASA's Website)
The final image? It's... okay. It's not Hubble quality, and it won't win any awards. But, considering I'm doing this from my balcony with gear that's probably older than some of your cars, it's something. And it keeps me from going completely stir-crazy. Just don't look too closely at the edges; that's where the alignment really falls apart. Maybe next time I'll try some fancy wavelet transform denoising, but honestly, I'll probably just take a nap.
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