In this paper, we consider two practical coded compressive imaging techniques. We investigate the optimal number of measurements under quadratic signal-to-noise-ratio (SNR) decrease. We focus on imaging scenarios in both real and complex vector spaces. In real vector spaces, we consider focal plane array (FPA) based super-resolution imaging with a constant measurement time constraint. Our model is comprised of a spatial light modulator and a low resolution FPA for modulating and sampling the incoming light intensity, respectively. We assume that signal energy decreases linearly with the number of measurements, and per-pixel signal energy increases linearly with reducing FPA resolution. We analyze this modality for different sensor sizes and number of measurements at different input SNR levels. Our results confirm that there is an optimal number of measurements maximizing the reconstruction pSNR, and taking more measurements beyond that point decreases the performance in a practical setting. %We also show SNR gain from larger pitch sizes on low-resolution FPA sensors can not be traded off for higher resolution in practical settings. This results in worse reconstruction performance for a low-resolution sensor compared to a high-resolution sensor even under optimal amount of measurements.

In complex vector spaces, we consider Magnetic Particle Imaging (MPI), another application for coded compressive imaging of in vivo magnetic nanoparticle agents. Coded scenes allow for fast calibration of the system matrix in this application. We investigate the peak SNR in the reconstructed images with respect to the number of coded scenes used in system calibration, and show the imaging performance for optimal number of coded scenes in noisy settings. The results show that more measurements do not necessarily translate into better performance in that imaging scenario either.