That's correct, for single images. Of course when we shoot multiple images of a stationary subject with sub-pixel offsets, then we can resort to things like super-resolution which will allow to increase the Nyquist frequency and thus shift the aliasing threshold. Likewise for S/N ratios we can use HDR exposure stacking, thus cherry picking from the lower shadow SNR levels of the longer exposures, or use exposure averaging which will reduce the noise threshold by the square root of the number of exposures.
My point was that while there is no general
method to remove aliasing in a single picture (and I am convinced that it might be proven that no such general method can ever be found), one can imagine non-general methods that exploit assumptions about typical images (or classes of images) and/or the camera sampling process to reduce aliasing without significantly affecting the desired signal, all in a perceptually pleasing manner.
I say this despite my education in signal processing telling me to always properly pre-filter and post-filter a discrete sampling process in the way suggested by Nyquist and Shannon some 90 and 60 years ago. Following the sampling theoreme takes all of the guess-work, statistics and perceptual complexity out, and you are left with a (theoretically) manageable linear filtering problem.
Interestingly, the sampling theoreme does not say that this filter has to be a lowpass filter, only that the bandwidth must be limited. As such, you could sample a scene by taking multiple exposures using an ideal sampler (approximated by a sensor where each sensel has small coverage, approaching a point-sampler), prefiltering using a set of bandpass filters at successively increasing passband (first [0...fs/2], then [fs/2...fs],...). The successive images would be aliased down to the baseband, but as the prefilter removes any non-aliased signal in that baseband, the aliasing components are uniquely resolvable. And a high-resolution image could be synthesized from the set. All according to a theoretical view, not considering the practical difficulty of actually doing it.