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Comparison of linear and nonlinear deconvolution algorithms for co-optimization of depth-of-field enhancing binary phase masks

Abstract : The depth-of-field of imaging systems can be enhanced by placing a phase mask in their aperture stop and deconvolving the image. In general, the mask is optimized using a closed-form image quality criterion assuming deconvolution with a Wiener filter. However, nonlinear deconvolution algorithms may have better performance, and the question remains as to whether a better co-designed system could be obtained from optimization with a criterion based on such algorithms. To investigate this issue, we compare optimization of phase masks with criteria based on the Wiener filter and on a nonlinear algorithm regularized by total variation. We show that the obtained optimal masks are identical, and propose a conjecture to explain this fact. This result is important since it supports the frequent co-design practice consisting of optimizing a system with a closed-form criterion based on linear deconvolution and deconvolving with a nonlinear algorithm.
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https://hal.archives-ouvertes.fr/hal-03128047
Contributor : Olivier Lévêque <>
Submitted on : Tuesday, February 2, 2021 - 12:10:53 AM
Last modification on : Wednesday, April 14, 2021 - 3:37:32 AM

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Olivier Lévêque, Caroline Kulcsár, François Goudail. Comparison of linear and nonlinear deconvolution algorithms for co-optimization of depth-of-field enhancing binary phase masks. OSA Continuum, OSA Publishing, 2021, 4 (2), pp.589-601. ⟨10.1364/OSAC.415925⟩. ⟨hal-03128047⟩

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