Output linear image?

[drmikevt]

Hello. I am new to Startools, but am impressed with some of the processing, esp. deconvolution. I still like my other workflows, but want to have Startools do some heavy lifting for me if possible. I would like to open a linear image in Startools, run wipe and deconvolution and then save the file in linear form. Is that possible? If so, I have not been able to figure out how to do it.

Thanks
Mike

[admin]

Hi Mike,

Welcome to the forums!
Thanks to StarTools' Tracking feature, you can get the engine to restore your dataset to a state where it has been Wiped and Deconvolved, but without further stretching you may have done to visualise it better.
To do so, hit the Restore button in the home screen and select Linear, Wiped, Deconvolved.
Now save this image as a TIFF file and you're done.

To get the most out of StarTools and truly understand its value to your processing stack, I can highly recommend familiarising yourself with Tracking and how it treats your data and preserves signal.

Hope this helps!

[drmikevt]

Thanks very much - this is exactly what I wanted to know. Your deconvolution process is very impressive - thanks for your work.

Any chance you might allow saving 32bit fit files in the future?

Mike

[admin]

[quote="drmikevt"]Thanks very much - this is exactly what I wanted to know. Your deconvolution process is very impressive - thanks for your work.

Any chance you might allow saving 32bit fit files in the future?[/quote

The FITS file format is really only meant for scientific data/measurements (e.g. photon counts), not interpretations thereof. Applying deconvolution (and especially StarTools' version thereof) turns the dataset into an "image" rather than keeping a dataset. Turning datasets into images in StarTools' main purpose as a post-processing application.

The reason why StarTools' decon works so well is because it takes into account how you've been stretching the image to visualise it. This gives StarTools extra information on how noise grain and ringing artefacts develop in the visual (non-linear domain). This in turn allows ST to target such unwanted influences much more precise in a much more meaningful way. This is the premise behind ST's signal evolution Tracking and it is applied throughout your processing at every step. Unfortunately, it also means that "changing horses" mid-processing will see you loose a great deal of those benefits. Processing your dataset from start-to-finish (ending with highly targeted noise reduction upon switching Tracking off) will give you the maximum benefit of this approach.