Do You Need to Understand Processing to Do Astrophotography?

Somebody with a new telescope asks this every week and gets two answers.

One is “yes, obviously, processing is half of astrophotography”. The other is “no, just use a script”. Both make the same mistake. They treat processing as one thing, when it is two things that happen to come together.

Two kinds of decision, mixed together

Open any processing workflow and you will be asked for something like forty numbers. They fall into two groups, and hardly anyone points this out.

The first group are measurements that look like choices.

Which rejection algorithm should combine your frames? That depends on how many frames you have and how much they vary. Forty frames and four hundred frames need different answers, and there is a correct one.

How far should the black point come down? That depends on what fraction of your image is sky. On a wide field of a small nebula it is around 98%, and the default in most software is roughly three times too high. That is why so many beginner images have a gray, washed-out background instead of a dark one.

How hard should you deconvolve? That follows from your measured star width and your signal-to-noise. Push past what the data supports and you get rings around bright stars.

Does drizzle help? Only if your frames were dithered, which is recorded in the frames themselves.

Which frames should be dropped? The ones with cloud, trails, lost focus or drift beyond the session’s own norm. All of that is measurable and none of it is opinion.

An experienced imager answers these by eye, quickly and fairly accurately, after years of practice. That is estimation rather than taste, and estimating quantities is something software does better than people.

The second group are real choices.

How bright do you want the image? How saturated? Should the nebula look as it would to an ideal eye, or be deliberately dramatic? Should the stars be prominent, or pulled back so the gas dominates? Is it a wide view with context or a tight crop?

Those are yours. Nobody can measure them for you, and a program that decided them would be making its photograph rather than yours.

What changed

For a long time both groups came together. To make the second kind of decision you first had to make the first kind several dozen times, correctly, and the only way to learn was to do it badly for a year.

Now the first group can be measured directly from your frames. Star sizes and shapes, drift, background level, gradient strength, noise, signal-to-noise, the fraction of the frame that is sky, whether there was cloud, whether the field rotated: all of it is in the data, and you don’t have to look for any of it.

Once those are measured, the parameters follow from your particular session rather than from a preset or a script that applies the same settings to everything. That is why a wide field and a tight galaxy crop end up with different black points.

The full sequence is written out step by step here, including what each step would otherwise take to learn.

What you still need to understand

Skipping the parameters does not mean skipping the knowledge. Some things no software will do for you.

Capture. Focus, tracking, exposure length, whether to dither, when the sky is worth shooting. Most disappointing astrophotographs are limited by the frames rather than the processing, and no software recovers a night that was out of focus. If you learn one half, learn this one. See why are my stars trailing and how long should subs be.

Calibration frames, at least in principle. You don’t need to know how a master flat is built. You do need to know that flats exist and that they fix the most visible fault in most beginner images. What darks, flats and bias do takes ten minutes to read and is worth more than any processing tutorial.

What a good image looks like. Taste stays yours. You develop it by looking at a lot of astrophotographs and noticing what you respond to.

How to read a result. Whether your stars are bloated, whether the background is too gray, whether the color is drifting. Diagnosing bad astrophotos covers the common faults and what causes each.

That is a much shorter list than “learn PixInsight”, and each item on it improves your next clear night as well as your next processing session.

The fair objection

“If you don’t learn it properly, you won’t be able to fix anything when it goes wrong.”

There is something to this. It matters less than it sounds because of the order in which you learn.

Learning processing before you have produced an image means learning abstractions with nothing to attach them to. You are told what deconvolution does before you have seen it do anything. Learning from a finished result works the other way round: here is the image, here is what was decided and the measurement behind each decision, now change one and see what happens.

The second order is faster, and most other technical crafts are learned that way. Nobody learns to cook by studying the Maillard reaction first.

The answer

No, you do not need to understand astrophotography processing to produce a good astrophotograph. You need to understand your capture, you need taste, and you need to be able to look at a result and say what is wrong with it.

The forty parameters were a toll booth. They existed because measuring the frames automatically used to be hard, not because setting them by hand was an art.

If you want photographs rather than a second hobby inside the first, you can skip them now. If you want the deep craft as well, it is still there and still worth learning. It just doesn’t have to be the entry fee.

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Akastroid is our attempt at this. It measures the session, runs the workflow, then lists every decision with the number behind it so you can overrule any of them. It is free with every feature and one export to try it on, and it runs entirely on your own computer without uploading anything.