We compare three optical architectures for compressive imaging: sequential, parallel, and photon sharing. Each of these architectures is analyzed using two different types of projection: (a) principal component projections and (b) pseudo-random projections. Both linear and nonlinear reconstruction methods are studied. The performance of each architecture-projection combination is quantified in terms of reconstructed image quality as a function of measurement noise strength. Using a linear reconstruction operator we find that in all cases of (a) there is a measurement noise level above which compressive imaging is superior to conventional imaging. Normalized by the average object pixel brightness, these threshold noise standard deviations are 6.4, 4.9, and 2.1 for the sequential, parallel, and photon sharing architectures, respectively. We also find that conventional imaging outperforms compressive imaging using pseudo-random projections when linear reconstruction is employed. In all cases the photon sharing architecture is found to be more photon-efficient than the other two optical implementations and thus offers the highest performance among all compressive methods studied here. For example, with principal component projections and a linear reconstruction operator, the photon sharing architecture provides at least 17.6% less reconstruction error than either of the other two architectures for a noise strength of 1.6 times the average object pixel brightness. We also demonstrate that nonlinear reconstruction methods can offer additional performance improvements to all architectures for small values of noise.