Side note: have always loved this image https://imgur.com/NZjWfWT of rainbows with UV and IR visible.
People way smarter than me have been able to achieve DIY spatial imaging with x-rays via compressed sensing [1] and with microwaves via phased arrays [2].
Optical wavelengths seem to be at a sweet spot of good angular resolution, varied natural sources, and harmless to humans.
However, the amount of light from the sun drops off exponentially away from the peak at green-blue (yellow-green, after atmospheric filtering). You'd also have to really fake the dynamic range a lot to get it to look any different from IR+Vis+NUV. (If there was 0.001% as much x-ray light as there is, say, red light, DNA could only exist in the lightless depths of the ocean.)
So, it would look like an IR+Vis photo (light falls off pretty fast in the UV, too), except the ones you've seen oversell the IR.
So it would look like a Vis-light photo, with slightly shinier objects in it.
Sorry.
If anyone is interested in some technical aspects of the "full frame" HSI I worked with, it's quite interesting. It had a 20MP Monochromatic Sensor that captured single-band 12-bit data behind an array of lenses that split the incoming spectral range (350-100nm) into 164 individual 4nm wide bands of light that hit 410x410px squares on the sensor. The sensor can capture from 350-1100, but the QE drops of really fast past about 850nm and the product limited the upper range to 1000nm. I'm sure I munged something there, but you should get the general idea. I highly recommend researching the space of HSI, it's fascinating.
Last thing to point out, when working with an HSI like this, one thing you can do is capture a "spectral fingerprint". Since you've gone from three bands on spectral intensity information to, in our case, 164 bands you have the ability to turn that high-density spectral data for each pixel into essentially a line graph. Using that information you can do matching against a database of known spectral fingerprints and identify materials and material properties really well. In the multi-spectra world you'll see this capability used to identify crop health. In the hyperspectral world you can identify so much more. For instance, it can see skin anomalies that aren't visible to the human eye. You can identify specific minerals in a picture of a bunch of rocks (you need up into the 2500nm range for this though). You can easily spot foreign objects on a conveyor of food items. Overall, it's a long list of capabilities and I'm certain there are many more uses we could discover if the imagers were cheaper. And if you are into the wider ML world (not just focused on LLMs I mean), you'll see ML Classification Models being trained on these spectral fingerprints as well.
Anyway, the "full-spectrum" is fascinating, especially when you are able to slice it thin.