Wavefront Randomization Improves Deconvolution
CoRR(2024)
摘要
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.
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