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.

翻译：成像系统的性能受限于光学像差，这些像差会导致最终图像模糊。数字校正技术（如反卷积）校正模糊的能力有限，因为场景中的某些空间频率无法被充分测量（即系统传递函数的“零点”）。我们证明，在成像系统中添加随机掩模可消除其对像差的依赖性，从而降低传递函数中出现零点的可能性，并因此减少反卷积过程中对噪声的敏感性。通过仿真，我们展示了该策略在多种像差类型、像差强度及信噪比条件下均能提升图像质量。