Your First Image in Akastroid: A Complete Walkthrough

Most astrophotography software assumes you already know the vocabulary. You are asked to pick a rejection algorithm before anyone has explained what rejection is, and to set a midtone transfer function on data you cannot yet see.

Akastroid takes the opposite position: it measures your data, decides, and then tells you what it decided and why. This walkthrough follows a real session from folder to finished image, and explains what is happening underneath at each step — because the decisions are more interesting than the buttons, and knowing them is what lets you tell a good result from a lucky one.

Before you start: what to put in the folder

Akastroid reads FITS from any telescope or astro camera, raw files straight off a DSLR or mirrorless body — CR2, CR3, NEF, ARW, DNG and others — and SER or AVI for planetary work. There is no conversion step. Point it at the files your camera actually wrote.

For a deep-sky session, put your light frames in a folder. If you shot calibration frames, put them in darks, flats and bias subfolders beside the lights, or leave them loose with their IMAGETYP headers intact — either way they are found automatically. If you have no calibration frames at all, carry on anyway. A stack of uncalibrated lights with its gradient removed is a real photograph.

Drop the folder on the window, or use Choose folder. Analysis starts immediately.

Stage one: grading

Before anything is combined, every frame is measured individually. Star count, focus, star roundness, background level and trailing.

What comes back is a table with a score per frame and, importantly, a reason attached to any rejection: star trailing, 2 satellite/aircraft trails, lost focus. Nothing is silently discarded — you can click any row and overrule the decision, in either direction.

Two things are worth reading here rather than skipping.

The eccentricity warning tells you whether your stars are oval, and whether that is tracking or optics. If it is consistent across the whole session it is coma, field curvature or camera tilt, and no frame is at fault — so no frames are rejected for it. If it varies frame to frame, it is tracking, and the bad ones are dropped. That distinction saves you from throwing away a whole session over a fixable optical problem.

The selected count tells you how aggressive the grading was. If it kept 109 of 134, that is normal. If it kept 40 of 134, look at why before proceeding — you may have a session where the sky changed halfway through, and that is worth knowing.

Stage two: two decisions before the long run

The panel before the main button asks at most two things, and both are shown with their answer already measured.

Drizzle reconstructs at higher resolution — but only recovers real detail if your frames were dithered, meaning they landed on slightly different sub-pixel positions between exposures. Akastroid measures your actual frames and tells you: “109 frames dithered across 57.1 px with a sub-pixel spread of 0.91 — enough to recover detail below the pixel grid”, or it disables the option and says “too few frames to drizzle”. It is not a preference. It is a property of the data you collected.

Photometric colour calibration matches your field against a catalogue of half a million stars with measured colours and sets the white balance from what those stars actually are. It needs the frames to record where the telescope was pointing. A Seestar, a Dwarf or any goto mount writes that; a bare DSLR on a tracker does not, and the option will say so.

If neither applies to your data, both are off and you press the button.

Stage three: what happens when you press it

This is the part worth understanding, because it is where the quality comes from.

Calibration. Darks, flats and bias applied where present. Then hot and cold pixel repair, statistically, whether or not you shot darks — because dark subtraction never catches cosmic ray hits, and most people have no darks at all. The discriminator is that a star is spread across several pixels by the optics while a defect stands alone.

Registration. Stars are matched between frames using triangle patterns, which survive translation, rotation and scale. This matters enormously on any alt-azimuth mount — every smart telescope — because the field rotates through the session. Translation-only alignment fails on that data in a way that looks like poor focus rather than an error.

Integration. The rejection method is chosen from two measurements: how many frames you have, and how much your sky level drifted through the night. A deep, steady session gets sigma clipping. A session where transparency moved gets linear-fit clipping, which expects a smooth drift and rejects only what departs from it. A shallow stack gets Winsorized clipping, because estimating a distribution from eight samples and then discarding values throws away real signal.

Gradient removal. A low-order surface is fitted to the sky and subtracted, keeping the overall level rather than driving the background to black.

Colour. Background neutralised by a per-channel shift — not a scale, because scaling drains the object along with the sky — then white balance from star colours, with each star’s local sky measured from an annulus around it rather than one number for the whole frame. That detail matters on a field like Orion, where stars sit on bright red nebulosity that would otherwise be counted as part of the star.

Deconvolution. The point spread function is measured from your own stars, and the strength is set by the signal-to-noise your stack actually has. It declines to run in three cases and says which: already sharper than the pixel scale records, too noisy to recover more than it amplifies, or stars elongated enough that a symmetric model would turn ovals into dumbbells.

Finishing. Stretch, denoise, local contrast, star reduction — every parameter adapted to what the image measures.

On a 134-frame DSLR session this takes a few minutes. Planetary video is faster; a 75-frame capture is about a second of actual stacking.

Stage four: three renders, and which to keep

You get Natural, Enhanced and Dramatic, all from the same data, switching instantly. They differ in stretch aggressiveness, saturation, contrast and star reduction — not in what was measured.

Enhanced is the default and right most of the time. Natural is the honest one for anything scientific. Dramatic is for sharing, and it is still bounded — nothing is invented.

The Stacked / Split / Processed control shows you the plain integration against the finished render. Use it. If the processed version has lost something the stack contained, that is visible immediately and nowhere else.

Stage five: the Details tab, which most people skip

This is where every decision is written down. Which stacking method and why. How much gradient was removed. Whether deconvolution ran, at what strength, and against what measured star width. Whether the colour calibration succeeded or fell back to a heuristic.

Read it once on your first image. It is the fastest available education in what actually happens to your data, and it tells you which of your problems are processing and which are capture.

What to override, and when

Most sliders should be left alone — they are on AUTO, and AUTO shows you the value the app measured rather than a generic default. Three are worth touching.

Black point, if the background still looks hazy. The automatic value comes from what fraction of your frame is sky; on a wide field that is 98%, and the target comes down accordingly.

Star reduction, if you disagree aesthetically. It is a taste question and the app has no opinion worth defending.

Saturation, for the same reason, in the same direction.

If you find yourself moving exposure strongly negative on every image, something upstream is wrong rather than a matter of taste — check the black point first.

Exporting

Maximum quality and Print write 16-bit TIFF at native resolution with no compression, which is what you want for anything you intend to work on further. A 480×256 planetary crop is around 740 KB and a full-frame DSLR stack is tens of megabytes; both are lossless, and the size difference is pixel count rather than quality.

There is also a Save the stacked image, unprocessed button. That writes the integration result before any finishing — the honest record of what your frames held, and the right starting point if you would rather finish in PixInsight or Siril.

If the result disappoints

Work through it in this order:

1. Look at the Stacked view. If the detail is not in the stack, no finishing recovers it, and the problem is capture or registration.
2. Read the Details log. It says what was decided. A stack that declined deconvolution because it was too noisy is telling you something about your exposure.
3. Check the frame table. If a third of your frames were rejected, the session had a problem worth understanding.
4. Only then reach for a slider.

That order is the difference between processing and guessing, and it is the same order an experienced imager works through — they have simply internalised it.