Extended depth of focus for high-end machine vision lenses by annular achromatic add-on diffractive elements

被引:3
|
作者
Suszek, Jaroslaw [1 ]
Makowski, Michal [1 ,3 ]
Kolodziejczyk, Andrzej [1 ]
Wlodarczyk, Filip [1 ]
Sobczyk, Artur [1 ]
Nurczyk, Piotr [1 ]
Duda, Przemyslaw [1 ]
Starobrat, Joanna [1 ]
Beck, Romuald [2 ]
机构
[1] Warsaw Univ Technol, Fac Phys, 75 Koszykowa, PL-00662 Warsaw, Poland
[2] Ctr Adv Mat & Technol CeZaMat, 19 Poleczki, PL-02822 Warsaw, Poland
[3] Warsaw Univ Technol, Fac Phys, Politech Warszawska, 75 Koszykowa, PL-00662 Warsaw, Poland
关键词
Imaging; Depth of focus; Diffraction; Optical character recognition; FIELD; PRESBYOPIA;
D O I
10.1016/j.optlaseng.2022.107445
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In modern machine vision systems high-end lens objectives allow superior imaging on state-of-the-art sensor arrays due to highly corrected optical aberrations and large apertures. The resultant shallow depth of focus is a disadvantage, usually leading to programmatic attempts of extending it, with compromised image resolution. Here we present a simple optical system equipped with novel, annular peripheral add-on diffractive elements, allowing highly extended range of sharp imaging without any pre-or post-processing. The separation of main refractive focusing power from the proposed filters enables sparse kinoform zones leading to easy manufactur-ing and negligibly low chromaticity in polychromatic imaging. The undisturbed central region of the refractive objective sustains the full native imaging quality of the test Zeiss Otus lens. Convergent numerical and experi-mental results are presented, showing improved readability of alphanumerical symbols in a wide range of w 20 defocus parameters up to 24 wavelengths. Robust neural network-based method of image quality assessment is also proposed based on statistical optical character recognition, allowing high dynamics and easy implementation in experimental use as compared to established methods.
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页数:10
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