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 due to the aberrations ('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 reducing the sensitivity to noise during deconvolution. and consequently result in lower 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，这类像差会导致图像模糊。数字校正技术（如去卷积）在消除模糊方面能力有限，因为像差会导致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照射像差的依赖性，降低传递函数出现零点的概率，从而减少去卷积过程中对噪声的敏感度。仿真结果表明，该策略能够在多种像差类型、像差强度及信噪比条件下提升图像质量。