The notions of spatial resolution and signal-to-noise are central to most forms of imaging. However, it appears that rigorous definitions of these quantities, which would be general enough to be useful in a broad range of imaging problems, while being also sufficiently specific to enable precise quantitative evaluation of the relevant properties of imaging systems, have been somewhat lacking. This is particularly true in respect to many modern forms of imaging that include digital processing of the acquired imaging data as an integral step leading to final images presented to the enduser. In the present paper, both the well-known historical definitions of spatial resolution and some more recent approaches suitable for many modern computational imaging techniques are discussed. An intrinsic duality of the spatial resolution and signal-to-noise exists in almost all types of imaging, with the related uncertainty relationship determining the inevitable trade-offs between the two quantities. Examples are presented with applications to some forms of X-ray imaging. Relations to Shannon and Fisher information capacity of imaging systems and super-resolution are also briefly discussed.