Conventional wavefront correction uses direct wavefront sensing methods such as the Shack-Hartmann sensor to
measure the wavefront at the pupil of the system. Image sharpening is an indirect wavefront sensing method where the
wavefront correction is performed using measurements from the image plane. Wavefront correction using image
sharpening is advantageous in systems where a point source isn't available or where the number of optical components
needs to be reduced by using the scientific camera that is already in place. Correction is performed by measuring the
sharpness value as the correction device, such as a deformable mirror, cycles through until the sharpness value is
maximized and continues to adapt as the aberrations change. A sharpness metric, or definition, is needed to measure the
sharpness value such that it reaches a maximum when aberrations are minimized. This work investigates the use of the
Fourier transform of the image, the image spatial frequency spectra, as a Fourier-based sharpness metric. The image
spatial frequency spectra is obtained two ways, digitally by computing the Fourier transform of the image plane and
optically with a coherent source by using the Fourier transform properties of a convex lens. Affects of aberrations on the
intensity at various spatial frequencies are investigated to obtain a sharpness metric that reaches a maximum and
aberration strengths decrease. Results from experimentation of various optical configurations are presented to evaluate
the performance of these Fourier-based metrics.
Undesirable turbulence effects present during propagation and imaging through the atmosphere are often reduced using
adaptive optics. Many current adaptive optics systems use the Shack-Hartmann wavefront sensor requiring
measurement and reconstruction of the incoming wavefront at the pupil plane. Indirect wavefront sensing methods, such
as image sharpening, are based on data obtained from the image plane. We are developing an image sharpness sensor
based on the Fourier spectrum of an image. High spatial frequencies contain information about the edges and fine detail
of the image. Our premise is the maximizing of the high spatial frequencies will sharpen the image. In our setup the
Fourier transform of the image is generated optically (and essentially instantaneously) and then various spatial-frequency
bands are filtered with an opaque mask. The remaining Fourier spectrum is integrated optically and a sharpness signal is
measured with a single photodetector. The collected sharpness value is used in a closed-loop to control the deformable
mirror until the sharpness is maximized. We have constructed both a simulation and a laboratory experiment to study
the sensor and its performance in an adaptive optics system.
Conventional adaptive optics systems use direct wavefront sensing such as the Shack-Hartmann sensor requiring a point
source such as a natural star or a laser guide star. In situations where a natural guide star isn't available or a laser guide
star isn't practical it is beneficial to use an indirect wavefront sensing approach based upon information in the image
itself. We are developing an image sharpness sensor using information found in the Fourier spectrum of the image.
Since high spatial frequencies contain information about the edges and fine detail of the image our premise is that
maximizing the high spatial frequencies will sharpen the image. The Fourier transform of the image is generated
optically (and essentially instantaneously) and then various spatial-frequency bands are filtered out with an opaque
mask. The remaining Fourier spectrum is integrated optically resulting in a single sharpness signal from a photodetector.
The collected sharpness value is used in a closed-loop to control the deformable mirror until the sharpness is maximized.
We have created a simulation to study the sensor and its performance in an adaptive optics system; results and
limitations will be discussed.