I was wondering; if all Starlink satellites had high-end cellphone quality cameras modules on simple telescopes, could the resulting images be combined and processed in such a way that a super-resolution imaging network be formed?
Could something like this have some competitive advantages, at least in some cases?
Relevant:
https://en.wikipedia.org/wiki/Event_Horizon_Telescope - Admittedly a global network of radio telescopes.
So, restricting myself to space observations:
No, for several reasons.
I. From an astronomical standpoint, cellphone cameras are terrible imagers. By themselves, they have tiny apertures -- typically 1 or 2 mm in diameter. Larger apertures do two things: improve the maximum possible angular resolution, and gather more light. The resolution scales with the diameter of the aperture; the Hubble Space Telescope (HST), with its 2.4-meter-diameter main mirror, has a resolution about two thousand times better than a cell-phone camera. The light-gathering power scales with the area of the aperture; this means that HST has the light-gathering power of about 5 million 1-mm-aperture cell-phone cameras.
Now, you did say "on simple telescopes", which implies that you're using the cell-phone camera just for its imager (the image sensor and the accompanying optics). But now you have to spend extra money on the actual telescope, including the special optics that send its light into the camera module; this telescope will probably be at least a meter in size and mass hundreds of kilograms, which means it will be at least as large as the Starlink spacecraft itself.
There are other problems, such as the lack of user-selectable filters. The WFC3-UVIS (UV + optical) camera on HST has about 60 different filters, for use in answering different kinds of scientific questions; a cell-phone camera sensor has no filters except for the per-pixel R, G, and B filters that consumer-use camera image sensors have. Even if you did add a filter module in front of the camera module, the fixed per-pixel RGB filters would mean that only 1/3 of the pixels would actually be usable at a given time (e.g., if you selected a "reddish" filter, only the pixels with R per-pixel filters would see any light).
Cell-phone cameras also have noisy electronics, resulting in noisy images. This is because typical cell-phone camera use involves scenes absolutely flooded with light (from an astronomer's perspective, anyway). The extra noise from the electronics is generally not noticeable in such cases. But if you're trying to observe faint astronomical objects, it actually matters. Astronomical visible-light imagers are both higher quality and cooled to liquid-nitrogen temperatures to reduce the electronic noise.
II. There's more to making a working astronomical space telescope than just sticking a camera module on a satellite. You need to be able to point the whole thing very precisely at your target, and keep it pointed in the correct direction while taking an image -- even though the satellite is moving rapidly through space. To do this, you need auxiliary ("guide") cameras and sensors, and computers to analyze the images of stars seen by the guide cameras and compute the necessary adjustments, and some means of rotating the satellite to keep it pointed properly, via gyroscopes, reaction wheels, or small thrusters.
III. "Super-resolution imaging networks" are not a thing -- except in the case of interferometric arrays (of which the Event Horizon Telescope is an example). But these work by preserving and combining the phase information of the incoming light from multiple telescopes. In the case of radio telescopes, the phase changes slowly enough that you can record it and combine it all later on a (super)computer. In the case of the EHT, the recorded data from a few days' worth of observations was so voluminous it was loaded onto hard drives that were flown to a central processing center.
Optical light changes phase much too fast to be feasibly recorded (and if you could, how would you transmit the information?), so the combination has to be done in real time by sending the light from different telescopes to a central instrument. So you don't want a "camera" recording images on each satellite; instead you want some means of redirecting the incoming light to a special central satellite where the light beams are combined. The combining has to be done with exquisite precision. This is possible on the ground, where none of the telescopes are moving; in orbit, with all the satellites constantly moving relative to each other, this would nightmarishly difficult.
(Note that I haven't mentioned anything about using "neural networks" or other forms of machine learning. That's because those would be useless, since they're meant to produce plausible-looking invented data, and what you want is real data -- what's actually out there in space right now.)
This question seems to specifically be about ground observation, but the underlying limitations are the same as those for astronomical imaging.
By combining multiple images, you can do things like:
What you can't do is increase resolution beyond the diffraction limit of the imaging optics. At best, you can produce a better image of the diffraction-blurred product of the optics.
Machine learning techniques can be used to guess at what features might produce the blurred image, but you're not truly imaging those features. If you know a vehicle is a particular make and model, you can synthesize a sharper image of it, but this is adding information to the image, not extracting more from it. A neural network will look for what it's been trained to find, rather than what's really there...in the end, you've just automated pareidolia. Look at DeepDream for extreme examples of this in action.
