As the name suggests, StarNet uses neural network for the job[3], and at least yet hasn't been trained on Webb data AFAIK, so might not do a good job there.
There also seems to be a similar tool with the same name over on github[4], but seems to be a separate effort.
[1]: https://www.starnetastro.com/
[2]: https://astrobackyard.com/starnet-astrophotography/
See for example: https://twitter.com/NGC3314/status/1582442367482634242?t=Eaa...
1. The hexagonal mirrors. Trade off is larger mirrors, and the ability to fold the telescope. 2. The struts holding the secondary mirror. Basically can't get rid of this without a very different telescope design.
Causes of both are the best design for getting a lot of light into a space telescope's sensors. In practice has less impact on the science than you might think. The diffraction spikes are much fainter than the main image. It's only bright foreground stars - which the James Webb isn't trying to image anyways - that cause visible spikes.
Not that easy if "the camera" is JWST.
Why build one when you can have two at twice the price?