You were correct in that the model won't be able to reconstruct any missing information with complete certainty (an intuitively impossible task). As Oliver Mason mentioned, it is estimating what a similar image (from the training data it has been exposed to) would have looked like (I should note that in the vast majority of cases, we don't/can't actually know what the model's internal representation looks like or how it works, only how to get there - see explainable AI).
In other words, it attempts to match the image it is given to a "latent space" (different terms are sometimes used for the same concept, but you may find the links in the article helpful - also see this question) that it has approximated (representing the data it was trained on) and mapped to the output space (i.e., the "high-resolution" images).
In theory it is possible that AI could be used to enhance certain features of crime scene images (e.g., sharpening the contours of a face so a detective can better recognize the person) but again, this only approximates the "true" data. In practical settings, it is more likely that the raw data is fed into a model designed specifically for achieving some end goal (for example, facial recognition) and humans are taken out of the loop once a sufficiently accurate model is available.
Generally, "highly educated guessing" is an appropriate summary for this type of method and you are right in your assumption that this limits its usefulness in certain applications where precision is important. Though this is somewhat tangential, you might find machine learning-based computational fluid dynamics (e.g., this paper) enlightening; while most machine learning techniques (anything involving neural networks, which are inherently approximators and also often nondeterministic) cannot produce scientifically rigorous simulations in the way traditional algorithms might, they can speed up some of the processing/analysis in the way a cleverly devised heuristic might.
Here are a couple articles you might find informative (some of these are pretty sparse in information relating to machine learning, but most have links to in-depth treatments):