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Imaging is an important means by which information is gathered regarding thephysical world. Spatial resolution and signal-to-noise ratio are underpinningconcepts. There is a paucity of rigorous definitions for these quantities,which are general enough to be useful in a broad range of imaging problems,while being also sufficiently specific to enable precise quantitativeevaluation of the relevant properties of imaging systems. This is particularlytrue for many modern forms of imaging that include digital processing of theacquired imaging data as an integral step leading to final images presented toan end-user. Here, both the well-known historical definitions of spatialresolution and some more recent approaches suitable for many forms of moderncomputational imaging are discussed. An intrinsic duality of spatial resolutionand signal-to-noise exists in almost all types of imaging, with the relateduncertainty relationship determining a trade-off between the two quantities.Examples are presented with applications to super-resolution imaging, inlineholography and ghost imaging.

Spatial resolution, noise and information in the computational-imaging era