Abstract

In a standard Shack–Hartmann wavefront sensor, the number of effective lenslets is the vital parameter that limits the wavefront restoration accuracy. This paper proposes a wavefront reconstruction algorithm for a Shack–Hartmann wavefront sensor with an insufficient microlens based on an extreme learning machine. The neural network model is used to fit the nonlinear corresponding relationship between the centroid displacement and the Zernike model coefficients under a sparse microlens. Experiments with a ${6} \times {6}$ lenslet array show that the root mean square (RMS) relative error of the proposed method is only 4.36% of the initial value, which is 80.72% lower than the standard modal algorithm.

© 2020 Optical Society of America

Full Article  |  PDF Article

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (13)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (2)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (7)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription