To have exactly zero aliasing, you need to reduce energy >= samplerate/2 to exactly zero (assuming that you are interested in the lowpass part). I believe that is usually impossible, but say that the residue is <-48dB below the desired lowpass signal. Then your aliasing noise is in the ballpark of an 8-bit quantizer, which might be enough for some applications.
When a camera is exposed for some time, you have smearing (lowpass filtering) due to camera movement, scene movement, atmospheric effects, diffraction in optics, non-ideal optical blur, anti-aliasing filter in front of the sensor, and the fact that sensels are more like «area integrators» than point samplers. Allign those factors «right» and the amount of aliasing can be kept in check.
Note that most cameras use a «Bayer» filter in front of the sensor. If you are pessimistic, you might want to use the «blue» channel with 1/2 the nominal resolution in each dimension and shortest wavelengths in order to see where aliasing might occur.
I have seen claims that a 24x36mm «fullframe» camera would need on the order of 500MP before all spatial information that can theoretically be conveyed by a perfectly focused f/0.7 lens made out of unobtainium within the visible spectrum is resolved.
Note also that imagery often strays far away from the theoretical ideals of Nyquist & co. I guess that comes down to the sensory apparatus in our eyes not applying (the equivalent of) a 1024-sample space-frequency transform prior to analysis. Rather, I think that we have smallish «edge» and feature detectors with low frequency resolution, and a preference for high contrast - even «fake» high contrast stemming from aliasing.