Eye movements present during acquisition of a retinal image with optical coherence tomography (OCT) introduce
motion artifacts into the image, complicating analysis and registration. This effect is especially pronounced in highresolution
data sets acquired with adaptive optics (AO)-OCT instruments. Several retinal tracking systems have been
introduced to correct retinal motion during data acquisition. We present a method for correcting motion artifacts in AOOCT
volume data after acquisition using simultaneously captured adaptive optics-scanning laser ophthalmoscope (AOSLO)
images. We extract transverse eye motion data from the AO-SLO images, assign a motion adjustment vector to
each AO-OCT A-scan, and re-sample from the scattered data back onto a regular grid. The corrected volume data
improve the accuracy of quantitative analyses of microscopic structures.
Adaptive optics (AO) is essential for many elements of the science case for the Thirty Meter Telescope (TMT). The
initial requirements for the observatory's facility AO system include diffraction-limited performance in the near IR, with
50 per cent sky coverage at the galactic pole. Point spread function uniformity and stability over a 30 arc sec field-ofview
are also required for precision photometry and astrometry. These capabilities will be achieved via an order 60×60
multi-conjugate AO system (NFIRAOS) with two deformable mirrors, six laser guide star wavefront sensors, and three
low-order, IR, natural guide star wavefront sensors within each client instrument. The associated laser guide star facility
(LGSF) will employ 150W of laser power at a wavelength of 589 nm to generate the six laser guide stars.
We provide an update on the progress in designing, modeling, and validating these systems and their components over
the last two years. This includes work on the layouts and detailed designs of NFIRAOS and the LGSF; fabrication and
test of a full-scale prototype tip/tilt stage (TTS); Conceptual Designs Studies for the real time controller (RTC) hardware
and algorithms; fabrication and test of the detectors for the
laser- and natural-guide star wavefront sensors; AO system
modeling and performance optimization; lab tests of wavefront sensing algorithms for use with elongated laser guide
stars; and high resolution LIDAR measurements of the mesospheric sodium layer. Further details may be found in
specific papers on each of these topics.
We present a model for MEMS deformable mirrors (DMs) that couples a 2-dimensional, linear 4th order partial
differential equation for the DM facesheet with linear spring models for the actuators. We estimate the
parameters in this model using the method of output least squares, and we demonstrate the effectiveness of this
approach with data from a 140-actuator MEMS test mirror produced at Boston University. A scheme for robust,
computationally efficient open-loop control, which is based on this model, is also presented.
In this paper we present a new stochastic model for time-varying turbulence. The model can be viewed as a linearization of the Navier-Stokes equation, with deterministic drift and diffusion terms, plus an additional stochastic driving term. Fixed-time realizations of the model have Kolmogorov statistics, but the diffusion and stochastic driving terms yield "boiling" behavior that is different from the Taylor frozen flow model.
In this paper we present a Fourier-domain preconditioned conjugate gradient algorithm for the fitting step in Multi-Conjugate Adaptive Optics (MCAO) for extremely large telescopes. This algorithm is fast and robust, and it is convenient to implement with parallel processing in a real-time system. Simulation results are presented for an MCAO system for a 30-meter telescope with 2 deformable mirrors.
In this paper we present a mathematical model for a point-actuated, continuous facesheet deformable mirror. The model consists of a single partial differential equation for the facesheet coupled with a number of nonlinear algebraic constraints (one constraint per actuator). We also present a nonlinearly constrained quadratic minimization problem whose solution gives the quasi-steady state control for the mirror, given a target wavefront aberration.
In this paper, we provide an overview of the adaptive optics (AO) program for the Thirty Meter Telescope (TMT) project, including an update on requirements; the philosophical approach to developing an overall AO system architecture; the recently completed conceptual designs for facility and instrument AO systems; anticipated first light capabilities and upgrade options; and the hardware, software, and controls interfaces with the remainder of the observatory. Supporting work in AO component development, lab and field tests, and simulation and analysis is also discussed. Further detail on all of these subjects may be found in additional papers in this conference.
This paper contains a review of sparse matrix methods for open-loop wavefront estimation in astronomical adaptive optics systems with a large number of degrees of freedom. We address shortcomings of existing sparse methods for multiconjugate adaptive optics and propose some alternative approaches. We also review certain closed-loop control schemes, dubbed pseudo open-loop control (POLC), that make use of open-loop linear algebra, and we propose an extension of POLC that makes use of knowledge of atmospheric dynamics to carry out predictive estimation in closed loop.
The multi-conjugate adaptive optics (MCAO) systems proposed for future giant telescopes will require new, computationally efficient, concepts for wavefront reconstruction due to their very large number of deformable mirror (DM) actuators and wavefront sensor (WFS) measurements. Preliminary versions of such reconstruction algorithms have recently been developed, and simulations of MCAO systems with 9000 or more DM actuators and 33000 or more WFS measurements are now possible using a single desktop computer. However, the results obtained to date are limited to the case of open-loop wavefront
reconstruction, and more work is needed to develop computationally efficient reconstructors for the more realistic case of a closed-loop MCAO system that iteratively measures and corrects time-varying wavefront distortions. In this paper, we describe and investigate two reconstruction concepts for this application. The first approach assumes that knowledge of the DM actuator command vector and the DM-to-WFS influence matrix may be used to convert a closed-loop WFS
measurement into an accurate estimate of the corresponding open-loop measurement, so that a standard open-loop wavefront reconstructor may be applied. The second approach is a very coarse (but computationally efficient) approximation to computing the minimum variance wavefront reconstructor for the residual wavefront errors in a closed-loop AO system. Sample simulation results are presented for both concepts with natural guide star (NGS) AO and laser guide star (LGS) MCAO systems on 8- and 32-meter class telescopes. The first approach yields a stable control loop with closed-loop performance comparable to the open-loop estimation accuracy of the classical minimum variance reconstructor. The second approach is unstable when implemented in a type I servo system.
Multi-conjugate adaptive optical (MCAO) systems with from 10,000 to 100,000 degrees of freedom have been proposed for future giant telescopes. Using standard matrix methods to compute, optimize, and implement wavefront reconstruction algorithms for these systems is impractical, since the number of calculations required to compute (apply) the reconstruction matrix scales as the cube (square) of the number of AO degrees of freedom. Significant improvements in computational efficiency are possible by exploiting the sparse and/or periodic structure of the deformable mirror influence matrices and the atmospheric turbulence covariance matrices appearing in these calculations. In this paper, we review recent progress in developing an iterative sparse matrix implementation of minimum variance wavefront reconstruction for MCAO. The basic method is preconditioned conjugate gradients, using a multigrid preconditioner incorporating a layer-oriented, iterative smoothing operator. We outline the key elements of this approach, including special considerations for laser guide star (LGS) MCAO systems with tilt-removed LGS wavefront measurements and auxiliary full aperture tip/tilt measurements from natural guide stars. Performance predictions for sample natural guide star (NGS) and LGS MCAO systems on 8 and 16 meter class telescopes are also presented.
Multi-conjugate adaptive optics (MCAO) systems with 104-105 degrees of freedom have been proposed for future giant telescopes. Using standard matrix methods to compute, optimize, and implement wavefront control algorithms for these systems is impractical, since the number of calculations required to compute and apply the reconstruction matrix scales respectively with the cube and the square of the number of AO degrees of freedom. In this paper, we develop an iterative sparse matrix implementation of minimum variance wavefront reconstruction for telescope diameters up to 32m with more than 104 actuators. The basic approach is the preconditioned conjugate gradient method, using a multigrid preconditioner incorporating a layer-oriented (block) symmetric Gauss-Seidel iterative smoothing operator. We present open-loop numerical simulation results to illustrate algorithm convergence.
For the numerical solution of large linear systems, the preconditioned conjugate gradient algorithm can be very effective if one has a good preconditioner. Two distinctly different approaches to preconditioning are discussed for solving systems derived from continuous linear operators of the form K + (alpha) L, where K is a convolution operator, L is a regularization operator, and (alpha) is a small positive parameter. The first approach is circulant preconditioning. The second, less standard, approach is based on a two-level decomposition of the solution space. A comparison of the two approaches is given for a model problem arising in atmospheric image deblurring.
Phase diversity is a technique for obtaining estimates of both the object and the phase, by exploiting the simultaneous collection of two short-exposure optical images, one of which has been formed by further blurring regularized variant of the Gauss-Newton optimization method for phase diversity-based estimated when a Gaussian likelihood fit-to-data criterion is applied. Simulation studies are provided to demonstrate that the method is remarkably robust and numerically efficient.
We apply a total variation minimization technique for the deblurring of 2D images. This yields an integro-differential equation with a nonlinear elliptic partial differential operator. In this paper we discuss a numerical scheme for the efficient solution of this integro-differential equation. Results are presented for a doconvolusion problem arising in ground-based astronomical imaging.