  In this paper, we improve image reconstruction in a single-pixel scanning
system by selecting an detector optimal field of view. Image reconstruction is
based on compressed sensing and image quality is compared to interpolated
staring arrays. The image quality comparisons use a "dead leaves" data set,
Bayesian estimation and the Peak-Signal-to-Noise Ratio (PSNR) measure.
  Compressed sensing is explored as an interpolation algorithm and shows with
high probability an improved performance compared to Lanczos interpolation.
Furthermore, multi-level sampling in a single-pixel scanning system is
simulated by dynamically altering the detector field of view. It was shown that
multi-level sampling improves the distribution of the Peak-Signal-to-Noise
Ratio.
  We further explore the expected sampling level distributions and PSNR
distributions for multi-level sampling. The PSNR distribution indicates that
there is a small set of levels which will improve image quality over
interpolated staring arrays. We further conclude that multi-level sampling will
outperform single-level uniform random sampling on average.
