Your telescope did the hard part.Let Akastroid do the rest.Stacks hundreds of frames.Removes light pollution.Finds your target by name.Rejects the ruined frames.Never uploads a thing.

It reads what your camera writes

FITS from a Seestar or ZWO. Canon CR2, Nikon NEF, Sony ARW and a dozen more straight off a DSLR. SER and AVI for planetary. No converting, no exporting first.

It decides, and tells you why

Which frames to keep, which stacking method, how far to stretch, how hard to denoise — measured from your data, not guessed. Every decision is listed with its reason.

It throws out the bad frames

Clouds, satellite trails, lost focus, tracking slips. Each rejection says what was wrong, and you can overrule any of them with a click.

Colour from real measurements

Akastroid carries half a million catalogued stars with measured colours, matches your field against them, and sets the white balance from what those stars actually are.

Planets, properly

Lucky imaging with multi-point alignment, atmospheric dispersion correction and wavelet sharpening. One app instead of four.

Nothing is invented

No generative AI paints in detail that was never there. Every pixel traces back to a photon your telescope collected.

If you are starting out

The obstacle between you and a photograph is not your telescope — it is a vocabulary. Sigma clipping, midtone transfer, background extraction: every tool asks you to choose before anyone explains what the choice means.

Akastroid measures your data and decides, then lists every decision with its reason. You get a real result on the first attempt, and the log is the fastest available education in what processing actually does.

If you already know your way around

You know photometric colour calibration is better than eyeballing white balance, and that a drifting night needs different rejection from a steady one. You skip both, because setting them up is fiddly and the payoff is invisible until it isn't.

Here they are measured and applied by default — the field matched against catalogued star colours, the rejection method chosen from how much your sky actually drifted.

If you have more data than time

A hundred-frame session finishes in a few minutes, which is enough to know whether it is worth an evening in PixInsight. Triage you could not previously afford.

And Save the stacked image, unprocessed writes the integration before any finishing — so Akastroid can be the front half of a workflow that ends wherever you already work.

Which frames to keep

Focus, star roundness, trailing, cloud and satellite trails, graded per frame. Every rejection states its reason, and oval stars consistent across the whole session are reported as optics rather than blamed on individual frames.

How to combine them

Shallow stacks cannot support aggressive rejection; a night whose transparency drifted needs a method that expects the drift. Both are read from your frames rather than asked.

How far to sharpen

The blur is measured from your own stars, and the strength follows from the signal-to-noise the stack actually has — including declining outright when it would amplify grain faster than it recovers detail.

Where the sky is

On a nightscape the horizon is found from where the stars are, never from brightness — a moonlit hillside is brighter than the sky above it, so brightness gets it backwards.

What colour things are

The field is matched against half a million catalogued stars with measured colours, and the white balance is fitted to what those stars actually are.

How dark the background sits

Set from what fraction of the frame is sky. On a wide field that is often 98%, and a target chosen for a half-full frame would lift the whole background to grey.