# FoFi – A free, automatic telescope focus finder software

At the time of publication this project is not even in ALPHA state. However, I feel now is the right time to publish it since it reached a state where at least the source code could be useful to others. You can find the source code on github here.

The software aims to support the amateur astronomer (and especially astrophotographer) with one of the most critical but also most annoying tasks: Finding the best focus position for the camera. The main goal is to provide a free and easy to use software that just does the job – automatically.

With a given configuration it should also be possible to execute “FoFi” from the command-line without requiring any user interaction. This way you can include a call to the Focus Finder into a script. This might be useful if the entire observation process should be automated and you want to re-focus from time to time to compensate the temperature drift.

Clear skies!

# An article from lost-infinity.com in the “Dark Sky Travels Magazine”!

Recently Dark Sky Travels Magazine contacted me and asked if they could publish one of my blog articles in their magazine. The article describes how one can use DeepSkyStacker to stack conventional DSLR camera RGB frames. Of course I didn’t say no and in the end it just happened and I saw my article on page 42/43 in Issue 4 of the DarkSkyTravels magazine!

I am very happy about that opportunity and the chance to share my experience this way.

Thanks and clear skies!

# Easy 2D Signal-to-Noise Ratio (SNR) calculation for images to find stars without extracting the background noise (C++)

This article shows how to calculate the 2D signal-to-noise ratio (SNR). Furthermore, it demonstrates how the $SNR$ can be used to decide if there is a potential star in the image.

Long story short – I was looking for a way to detect more or less reliably if a user selected a region which contains a star. I wanted to be able to clearly distinguish between the following two images:

### Solution with the CImg library

After a long journey I finally ended up with the following solution. It is based on the CImg library which in a way calculates the Signal-to-noise ratio (SNR):

CImg <uint16_t> image;
...
double q = image.variance(0) / image.variance_noise(0);
double qClip = (q > 1 ? q : 1);
double snr = std::sqrt(qClip - 1);

For the two images above the code gives the following results:

~/snr$./snr no_star.fits SNR: 0.0622817 ~snr$ ./snr test_star.fits
SNR: 1.5373

For many people this is where the journey ends. But for some of you it may just begin :). Follow me into the rabbit hole and find out why the solution shown above actually works…