Flux: Automated Astronomical Feature Recognition and Manipulation

2 images of a galaxy, the lower one sharpend by the flux module.
Flux sharpening by self similarity.

The Fractal Flux module allows for fully automated analysis and subsequent processing of astronomical images of DSOs.

The one-of-a-kind algorithm pin-points features in the image by looking for natural recurring fractal patterns that make up a DSO, such as gas flows and filaments. Once the algorithm has determined where these features are, it then is able to modify or augment them.

Two versions of the Lenna image above eachother, the lower one sharpened with the Flux module.
Flux Sharpening by self-similarity feature detection. Only areas that are deemed recurring detail are sharpened.

Knowing which features probably represent real DSO detail, the Fractal Flux is an effective de-noiser, sharpener (even for noisy images) and detail augmenter.

Detail augmentation through flux prediction can plausibly predict missing detail in seeing-limited data, introducing detail into an image that was not actually recorded but whose presence in the DSO can be inferred from its surroundings and gas flow characteristics. The detail introduced can be regarded as an educated guess.

It doesn't stop there however – the Fractal Flux module can use any output from any other module as input for the flux to modulate. You can use, for example, the Fractal Flux module to automatically modulate between a non-deconvolved and deconvolved copy of your image – the Fractal Flux module will know where to apply the deconvolved data and where to refrain from using it.


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