  The performance of an imaging system is limited by optical aberrations, which
cause blurriness in the resulting image. Digital correction techniques, such as
deconvolution, have limited ability to correct the blur, since some spatial
frequencies in the scene are not measured adequately (i.e., 'zeros' of the
system transfer function). We prove that the addition of a random mask to an
imaging system removes its dependence on aberrations, reducing the likelihood
of zeros in the transfer function and consequently decreasing the sensitivity
to noise during deconvolution. In simulation, we show that this strategy
improves image quality over a range of aberration types, aberration strengths,
and signal-to-noise ratios.
