## Model-based aberration correction in a closed-loop wavefront-sensor-less adaptive optics system |

Optics Express, Vol. 18, Issue 23, pp. 24070-24084 (2010)

http://dx.doi.org/10.1364/OE.18.024070

Acrobat PDF (934 KB)

### Abstract

In many scientific and medical applications, such as laser systems and microscopes, wavefront-sensor-less (WFSless) adaptive optics (AO) systems are used to improve the laser beam quality or the image resolution by correcting the wavefront aberration in the optical path. The lack of direct wavefront measurement in WFSless AO systems imposes a challenge to achieve efficient aberration correction. This paper presents an aberration correction approach for WFSlss AO systems based on the model of the WFSless AO system and a small number of intensity measurements, where the model is identified from the input-output data of the WFSless AO system by black-box identification. This approach is validated in an experimental setup with 20 static aberrations having Kolmogorov spatial distributions. By correcting *N* = 9 Zernike modes (*N* is the number of aberration modes), an intensity improvement from 49% of the maximum value to 89% has been achieved in average based on *N* + 5 = 14 intensity measurements. With the worst initial intensity, an improvement from 17% of the maximum value to 86% has been achieved based on *N* + 4 = 13 intensity measurements.

© 2010 Optical Society of America

## 1. Introduction

1. M. A. Vorontsov, G. W. Carhart, D. V. Pruidze, J. C. Ricklin, and D. G. Voelz, “Adaptive imaging system for phase-distorted extended source and multiple-distance objects,” Appl. Opt. **36**(15), 3319–3328 (1997). [CrossRef] [PubMed]

12. L. Sherman, J. Y. Ye, O. Albert, and T. B. Norris, “Adaptive correction of depth-induced aberrations in multiphoton scanning microscopy using a deformable mirror,” J. Microsc. **206**(1), 65–71 (2002). [CrossRef] [PubMed]

14. P. Marsh, D. Burns, and J. M. Girkin, “Practical implementation of adaptive optics in multiphoton microscopy,” Opt. Express **11**(10), 1123–1130 (2003). [CrossRef] [PubMed]

16. S. P. Poland, A. J. Wright, and J. M. Girkin, “Evaluation of fitness parameters used in an iterative approach to aberration correction in optical sectioning microscopy,” Appl. Opt. **47**(6), 731–736 (2008). [CrossRef] [PubMed]

13. M. J. Booth, M. A. A. Neil, R. Juskaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Nat. Acad. Sci. U.S.A. **99**(9), 5788–5792 (2002). [CrossRef]

17. D. Débarre, E. J. Botcherby, M. J. Booth, and T. Wilson, “Adaptive optics for structured illumination microscopy,” Opt. Express **16**(13), 9290–9305 (2008). [CrossRef] [PubMed]

18. D. Débarre, E. J. Botcherby, T. Watanabe, S. Srinivas, M. J. Booth, and T. Wilson, “Image-based adaptive optics for two-photon microscopy,” Opt. Lett. **34**(16), 2495–2497 (2009). [CrossRef] [PubMed]

21. M. J. Booth, “Wave front sensor-less adaptive optics: a model-based approach using sphere packings,” Opt. Express **14**(4), 1339–1352 (2006). [CrossRef] [PubMed]

18. D. Débarre, E. J. Botcherby, T. Watanabe, S. Srinivas, M. J. Booth, and T. Wilson, “Image-based adaptive optics for two-photon microscopy,” Opt. Lett. **34**(16), 2495–2497 (2009). [CrossRef] [PubMed]

*N*aberration modes can be corrected after 2

*N*+ 1 images.

*N*+ 2 predefined control signals and the corresponding

*N*+ 2 intensity measurements are collected. Aberration is estimated and corrected based on these

*N*+ 2 pairs of input-output data and the model of the WFSless AO system, by solving a nonlinear least squares (NLLS) optimization problem online. With new input-output data available, the aberration estimation and correction are refined iteratively. This approach is validated in a WFSless AO experimental setup and the performance of the resulting closed-loop system is evaluated.

## 2. System analysis

2. G. Vdovin, “Optimization-based operation of micromachined deformable mirrors,” Proc. SPIE **3353**, 902–909 (1998). [CrossRef]

3. M. A. Vorontsov, G. W. Carhart, M. Cohen, and G. Cauwenberghs, “Adaptive optics based on analog parallel stochastic optimization: analysis and experimental demonstration,” J. Opt. Soc. Am. A **17**(8), 1440–1453 (2000). [CrossRef]

21. M. J. Booth, “Wave front sensor-less adaptive optics: a model-based approach using sphere packings,” Opt. Express **14**(4), 1339–1352 (2006). [CrossRef] [PubMed]

*y*(

*k*) ∈ ℝ at time

*k*by adapting the control signal

*u*(

*k*) ∈ ℝ

*to the DM, i.e, where*

^{N}*u*(

*k*) can be the zonal or modal representation of the control signal, with dimension

*N*.

12. L. Sherman, J. Y. Ye, O. Albert, and T. B. Norris, “Adaptive correction of depth-induced aberrations in multiphoton scanning microscopy using a deformable mirror,” J. Microsc. **206**(1), 65–71 (2002). [CrossRef] [PubMed]

13. M. J. Booth, M. A. A. Neil, R. Juskaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Nat. Acad. Sci. U.S.A. **99**(9), 5788–5792 (2002). [CrossRef]

17. D. Débarre, E. J. Botcherby, M. J. Booth, and T. Wilson, “Adaptive optics for structured illumination microscopy,” Opt. Express **16**(13), 9290–9305 (2008). [CrossRef] [PubMed]

*a*is a constant. Apart from that, if the aberration is corrected within a short time, it is reasonable to consider the wavefront aberration as constant (e.g., when any single point in the specimen is imaged in scanning-type microscopes under normal operational conditions). This simplifies

_{i}*ϕ*(

_{x}*ξ*,

*η*,

*k*) as such that

*ϕ*(

_{x}*ξ*,

*η*) is time-independent.

*ϕ*(

_{m}*ξ*,

*η*,

*k*) can be written as where

*D*(

*ξ*,

*η*) represents the static linear response of the DM. This linear representation of the DM response is valid for most commonly-used DMs when they are appropriately linearized, e.g, taking the square root of the voltage as the control signal for electrostatic-actuated DM [2

2. G. Vdovin, “Optimization-based operation of micromachined deformable mirrors,” Proc. SPIE **3353**, 902–909 (1998). [CrossRef]

23. H. Song, G. Vdovin, R. Fraanje, G. Schitter, and M. Verhaegen, “Extracting hysteresis from nonlinear measurement of wavefront-sensorless adaptive optics system,” Opt. Lett. **34**(1), 61–63 (2009). [CrossRef]

*D*(

*ξ*,

*η*) can be considered as a mode of the DM deformation and

*u*(

*k*) contains all the modal coefficients. The column space of

*D*(

*ξ*,

*η*) forms a basis for

*ϕ*(

_{m}*ξ*,

*η*,

*k*). Different basis can be used (e.g., DM actuator basis, Zernike basis, Lukosz basis), depending on how the control signal

*u*(

*k*) is defined. For instance, if

*u*(

*k*) is same as the voltage applied to each actuator of the DM (i.e, zonal control), then

*D*(

*ξ*,

*η*) is the influence function of the DM; otherwise, Zernike modal control or Lukosz modal control can also be applied.

*u*(

*k*) to

*y*(

*k*) with the aberration

*ϕ*(

_{x}*ξ*,

*η*) absent, and at least

*N*+ 2 pairs of

*u*(

*k*) and

*y*(

*k*) collected with

*ϕ*(

_{x}*ξ*,

*η*) present, the aberration

*ϕ*(

_{x}*ξ*,

*η*) can be estimated in the basis defined by

*D*(

*ξ*,

*η*), as will be explained in Section 3.

## 3. Model-based aberration estimation and correction

### 3.1. Modeling of the WFSless AO system

*ϕ*(

_{m}*ξ*,

*η*) can not be measured in the WFSless AO system and

*D*(

*ξ*,

*η*) can not be obtained with high accuracy, it is difficult to get an accurate model of the real system from Eq. (6). The artifacts in the optical components may also degrade the accuracy of Eq. (6). As will be shown later on, since hundreds of times of intensity calculations are needed by our proposed algorithm to estimate the aberration, the computational complexity in Eq. (6) (e.g., two double integrals for each intensity calculation) will slow down the aberration correction speed. Therefore in our work the AO model is identified directly from

*u*(

*k*) and

*y*(

*k*) by black-box identification [24

24. M. Verhaegen and V. Verdult, *Filtering and System Identification: A Least Squares Approach*, (Cambridge University Press, Cambridge, USA, 2007). [CrossRef]

25. J. Sjöberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P. Glorennec, H. Hjalmarsson, and A. Juditsky, “Non-linear black-box modeling in system identification: a unified overview,” *Automatica*31(12), 1691–1724 (1995). [CrossRef]

*g*represents the static nonlinear wavefront-intensity mapping, including the double integral over the coordinate (

*ξ*,

*η*). The wavefront aberration

*ϕ*(

_{x}*ξ*,

*η*) can be split into two parts as Here

*ϕ*

_{1}(

*ξ*,

*η*) represents the part of

*ϕ*(

_{x}*ξ*,

*η*) lying within the range of

*D*(

*ξ*,

*η*) and Δ

*ϕ*(

_{x}*ξ*,

*η*) represents the part of

*ϕ*(

_{x}*ξ*,

*η*) which is orthogonal to the range of

*D*(

*ξ*,

*η*). It is assumed that the wavefront aberration can be represented by a finite low-order Zernike aberrations [13

13. M. J. Booth, M. A. A. Neil, R. Juskaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Nat. Acad. Sci. U.S.A. **99**(9), 5788–5792 (2002). [CrossRef]

26. M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high NA adaptive optical microscopy,” Opt. Express **12**(26), 6540–6552 (2004). [CrossRef] [PubMed]

27. G. Vdovin, O. Soloviev, A. Samokhin, and M. Loktev, “Correction of low order aberrations using continuous deformable mirrors,” Opt. Express **16**(5), 2859–2866 (2008). [CrossRef] [PubMed]

*ϕ*(

_{x}*ξ*,

*η*) can be neglected. As a result, Eq. (8) can be approximated by Substitute Eq. (9) into (7), we have Merging

*D*(

*ξ*,

*η*) and

*g*into one static nonlinear mapping

*f*, we can further simplify the system description as Equation (11) considers the aberration as a disturbance directly applied on the input

*u*(

*k*), which allows to identify the model of the WFSless AO system only based on

*u*(

*k*) and

*y*(

*k*) but meanwhile accounting for the influence of the aberration.

*u*(

*k*) and

*y*(

*k*), the nonlinearity in the system should be excited persistently by the input

*u*(

*k*). Random signals can then be used to excite the system for data collection. Since

*f*is identified only based on

*u*(

*k*) and

*y*(

*k*),

*y*(

*k*) should be collected with

*x*= 0. If

*x*=

*x*

_{0}≠ 0 (

*x*

_{0}is an unknown nonzero constant vector) during the data collection, then there is an offset of

*x*

_{0}in the estimated aberration, as will be seen in the next section. In practice, this aberration-free condition may be achieved after the calibration of the WFSless AO system, when the aberration of the WFSless AO system itself (system aberration, e.g., initial aberration in the DM, misalignment of the optical components) has been corrected and the aberration induced by external sources (e.g., air turbulence, high power heating or specimen) is still absent. The system aberration can be corrected by optimization algorithms like simplex algorithm, genetic algorithm, etc. Although optimization algorithm is used here for system aberration correction, the system aberration only needs to be corrected once during the operation of the WFSless AO system. Significant time can still be saved in correcting the external aberrations.

*u*(

*k*) and

*y*(

*k*), the model structure needs to be selected for the nonlinear black-box model. There is a very rich spectrum of possible descriptions for nonlinear black-box models, e.g., neural network [28, 29], fuzzy models [30], etc. Because a 2-layer neural network is able to model a broad range nonlinearities and, from practical point of view, it can be implemented and trained with the MATLAB Neural Network Toolbox [31

31. H. Demuth, M. Beale, and M. Hagan, *Neural Network Toolbox 5 User’s Guide*, (The MathWorks, Inc., 2007). [PubMed]

*N*neurons in the first layer and one in the second. The output

_{Q}*ŷ*(

*k*) of the neural network is determined as

*W*

_{2}∈ ℝ

^{NQ×N}and

*W*

_{1}∈ ℝ

^{1×NQ}contain the input and output weights of the neural network, respectively;

*s*

_{1}∈ ℝ

^{NQ×1}and

*s*

_{2}∈ ℝ are biases on the input and output neurons, respectively. tanh is the hyperbolic tangent function.

*N*should be defined by the user when constructing the neural network. Parameters

_{Q}*W*

_{1},

*W*

_{2},

*s*

_{1}and

*s*

_{2}are then optimized by training the neural network with sufficient data points

*u*(

*k*) and

*y*(

*k*). Details on training and validating the neural network can be found, for instance, in [28, 29].

### 3.2. Aberration estimation and correction

*x*present, if the WFSless AO system is excited by a certain number of inputs

*u*(

*k*),

*k*= 1, ⋯ ,

*K*(

*K*is the number of data points) and the intensity

*y*(

*k*),

*k*= 1, ⋯ ,

*K*, are collected, then

*x*can be estimated by solving a set of nonlinear equations as Here

*f̂*is the model of the WFSless AO system identified in previous step.

*a*is a scaling factor, accounting for the possible variation in the incident light power between the modeling and aberration estimation. For instance, in microscopes, the light power emitted or reflected by the specimen may vary from point to point. The obstructing layers of the specimen may also scatter, reflect or absorb the light passing through. Although we are only interested in

*x*for aberration correction,

*a*should also be estimated because it is unknown in Eq. (13).

*f̂*has some high-degree components. Alternatively, a numerical solution can be obtained by solving a nonlinear least squares (NLLS) problem as with

*Y*

_{[1,K]}and

*Ŷ*

_{[1,K]}constructed as Here

*â*and

*x̂*are the estimates of

*a*and

*x*, respectively. For given

*â*and

*x̂*, the intensity is estimated by

*ŷ*(

*k*) =

*âf̂*(

*x̂*+

*u*(

*k*)).

*K*concerning the accuracy of the aberration estimation and the correction speed. From one hand, inadequate data points can not give an accurate aberration estimation, for instance, more than one solutions may exist in Eq. (13) and the cost function

*J*(

*â*,

*x̂*) in Eq. (14) does not have a unique global minimum (see Fig. 2 for an illustration). From the other hand, if more data points are collected than necessary, then the correction speed will be slowed down. A theoretical analysis on this is difficult because several factors should be considered, e.g., the nonlinearity

*f*, the model uncertainty in

*f̂*, the measurement noise in

*y*(

*k*) and the values of the

*K*inputs. However, as a practical solution, aberration estimation and correction can be implemented in an iterative manner and the model-based aberration correction (MBAC) algorithm is described below.

- Before the aberration estimation, the WFSless AO system is initially excited by
*N*+ 2 control signals*u*(*k*) and the corresponding intensity measurements*y*(*k*) are collected. Here*N*+ 2 data points are collected for initialization concerning that*N*+ 1 unknowns need at least*N*+ 1 equations in Eq. (13) to have a unique solution if*f*were a linear function, and that nonlinear functions may need more equations in general. Since the aberration estimation and correction will be refined iteratively later on, these*N*+ 2 data points serves as an initial trial for the MBAC algorithm. A natural option for the first control signal is*u*(1) = 0, i.e., no correction by the DM. The other*N*+ 1 control signals should excite the aberrated system in such a way that rich information can be collected on the aberration*x*. Selection of such*N*+ 1 inputs has been investigated in [21]. The optimum distribution of the**14**(4), 1339–1352 (2006). [CrossRef] [PubMed]*N*+ 1 inputs can be geometrically interpreted as the*N*+ 1 vertices of a regular simplex in the*N*-dimensional space (see Appendix B of [21**14**(4), 1339–1352 (2006). [CrossRef] [PubMed] - From time
*k*=*N*+ 2 on, the aberration estimation (denoted as*x̂*(*k*– 1)) is given by Eq. (14), based on previous*K*=*k*– 1 control inputs and intensity measurements. The control input is then set as*u*(*k*) = –*x̂*(*k*– 1) to counter-react on the aberration and the corresponding intensity*y*(*k*) is measured. The newly-collected*y*(*k*) and*u*(*k*) are added into*Y*_{[1,K]}and*Ŷ*_{[1,K]}respectively in Eq. (15) and the aberration estimation can be refined by solving Eq. (14) with the latest*Y*_{[1,K]}and*Ŷ*_{[1,K]}. This estimation-correction-collection procedure can be repeated iteratively. The algorithm can be stopped when a certain criterion is met, for instance, when the improvement over the previous intensity measurement is less than a certain threshold value, or when the maximum number of intensity measurements is exceeded.

*f̂*and the measurement noise in

*y*(

*k*), the accuracy of the aberration estimation may be limited and the intensity may not reach its maximum by the MBAC algorithm. In this situation, other optimization algorithms like simplex algorithm, genetic algorithm, etc., can be used to continue searching for the optimum. Under the assumption that

*f̂*is a close approximation of

*f*, the MBAC algorithm will steer the DM to a point close to its optimum. This point can then be used as a new initial condition for desired nonlinear optimization method, like the simplex algorithm described in [33]. The initial simplex of the simplex algorithm is constructed around the control signal which gives the maximum intensity measurement in the MBAC algorithm. The hybrid algorithm (MBAC+Simplex) is described in pseudo code below. The MBAC algorithm stops after a fixed number of intensity measurements

*P*(

*P*is a user-defined number), to distinguish the intensity improvements due to the MBAC algorithm and due to the simplex algorithm. The simplex algorithm stops at time

*P̂*(

*P̂*is a user-defined number).

**MBAC+Simplex algorithm**(general description and

*pseudo code implementation*):

- Initialization of MBAC, i.e., collecting
*N*+ 2 data points*Set u*(1) = 0.*Set u*(*k*)*as in Appendix B of [21*= 2, ⋯ ,**14**(4), 1339–1352 (2006). [CrossRef] [PubMed]*N*+ 2.*Set â*(*k*) = 1,*with k*= 1, ⋯ ,*N*+ 2.*end*

- Aberration estimation and correction by MBAC
*for k*=*N*+ 3 :*P**p*= argmax_{p}*y*(*p*) ;*â*=_{init}*â*(*p*),*x̂*= −_{init}*u*(*p*);- [
*â*(*k*– 1),*x̂*(*k*– 1)] = argmin_{â,x̂}*J*(*â*,*x̂*)*as in Eq.(14), with initial conditions â*_{init}*and x̂*_{init}. *Set u*(*k*) = –*x̂*(*k*– 1),*excite the system with u*(*k*)*and collect y*(*k*).

*end*

- Aberration correction by the simplex algorithm
*p*= argmax_{p}*y*(*p*);*u*=_{c}*u*(*p*);*Construct simplex around u*_{c}*as u*(*k*) =*u*(*k*–*P*+ 1) +*u*_{c}*with k*=*P*+ 1, ⋯ ,*P*+*N*+ 1.*end*

## 4. Experimental setup

27. G. Vdovin, O. Soloviev, A. Samokhin, and M. Loktev, “Correction of low order aberrations using continuous deformable mirrors,” Opt. Express **16**(5), 2859–2866 (2008). [CrossRef] [PubMed]

*μ*m) is placed at the focal point of L3, followed by a photodiode (TSL250R-LF, TAOS, Korea) measuring the light intensity inside the pin hole. The high voltage amplifier (HVA, OKOTech, The Netherlands) has 40 channels, each with an output range of 0∼300 V, a voltage amplification of 80 at low frequencies and a −3dB bandwidth of 1 kHz. The control algorithm is implemented in MATLAB (Version 7.5.0.342, The MathWorks). Signal generation and data acquisition is accomplished by a dSPACE system (DS1006, dSPACE, Germany) with the digital-to-analog card (DS2103) output range of ±10 V, 14-bit and analog-to-digital card (DS2004) input range of ±10 V, 16-bit. Interfacing between MATLAB and the dSPACE system is done via MLIB (dSPACE, Germany).

*V*(

*k*) ∈ ℝ

^{37}, which is applied to 37 actuators of the PDM. The output of the WFSless AO system is the light intensity measurement

*y*(

*k*) ∈ ℝ from the photodiode. To reduce the uncertainty in the AO setup, a hysteresis compensator

*Ĥ*

^{−1}is implemented to compensate for the hysteresis in the PDM as described in [23

23. H. Song, G. Vdovin, R. Fraanje, G. Schitter, and M. Verhaegen, “Extracting hysteresis from nonlinear measurement of wavefront-sensorless adaptive optics system,” Opt. Lett. **34**(1), 61–63 (2009). [CrossRef]

*u*(

*k*) ∈ ℝ

*, the PDM is controlled in Zernike basis by*

^{N}*N*= 9 modes. This is accomplished by the matrix

*L*∈ ℝ

^{37×N}which transforms the modal control signal

*u*(

*k*) to the pseudo voltage

*V̂*(

*k*).

*L*is derived according to the Zernike polynomials description in [26

26. M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high NA adaptive optical microscopy,” Opt. Express **12**(26), 6540–6552 (2004). [CrossRef] [PubMed]

34. M. Loktev, D. Monteiroa, and G. Vdovin, “Comparison study of the performance of piston, thin plate and membrane mirrors for correction of turbulence-induced phase distortions,” Opt. Commun. **192**, 91–99 (2001). [CrossRef]

26. M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high NA adaptive optical microscopy,” Opt. Express **12**(26), 6540–6552 (2004). [CrossRef] [PubMed]

*u*(

*k*) as input and intensity measurement

*y*(

*k*) as output. The intensity measurement is fed into the controller and the control signal

*u*(

*k*) is calculated.

## 5. Experiments and results

- With the aberration generator absent, the WFSless AO system is calibrated using a simplex optimization algorithm. The system aberration is corrected by adapting the shape of the PDM such that the intensity measurement is maximized.
- The WFSless AO system is excited by random control signals
*u*(*k*) and the intensity measurements*y*(*k*) are collected. Based on*u*(*k*) and*y*(*k*), the WFSless AO system is modeled by a neural network as described in Section 3.1. - Aberration is introduced in the WFSless AO system by the aberration generator and corrected by the proposed MBAC+Simplex algorithm as described in Section 3.3. For a comparison, the simplex algorithm alone is also used to correct the aberration. Intensity improvements by these two algorithms are evaluated and compared.

### 5.1. System calibration

*y*(

*k*) by adapting the control signal

*u*(

*k*) as in Eq. (1). The sampling rate of the system during the calibration is

*f*= 50 Hz, which is much less than the resonance frequency of the PDM (about 1 kHz), so that the AO system is considered static. The maximum intensity measurement is denoted as

_{s}*y*, which is used to normalize intensity measurement in Section 5.3. The control signal which results in the maximal intensity measurement, denoted as

_{max}*u*

_{0}, is used as a bias in all the following experiments.

### 5.2. Modeling of the AO system

*u*(

*k*) in open-loop with the aberration generator absent and the intensity measurements

*y*(

*k*) are collected. The control signals

*u*(

*k*) distribute randomly within the operational range of the PDM, to give a persistent excitation. The sampling rate of the system is also 50 Hz.

*N*neurons in its first layer and one neuron in its second layer as in Eq. (12). The neural network is implemented and trained by MATLAB Neural Network Toolbox [31

_{Q}31. H. Demuth, M. Beale, and M. Hagan, *Neural Network Toolbox 5 User’s Guide*, (The MathWorks, Inc., 2007). [PubMed]

*W*

_{1},

*W*

_{2},

*s*

_{1}and

*s*

_{2}in Eq. (12) are optimized by minimizing the mean square of the fitting error, using Levenberg-Marquardt (LM) backpropagation algorithm, i.e.,

*N*is the number of data points for identification, in our case,

_{t}*N*= 6000.

_{t}*y*) is the variance of

*y*. Figure 4 shows the VAFs of the AO model with different number of neurons in the first layer. From this plot, it can be seen that VAF already reaches as high as 98.2% at

*N*= 20 for the identification set and 97.8% for the validation set, indicating that the neural network can model the AO system very accurately. The difference in VAF is negligible for

_{Q}*N*> 20. Therefore 20 neurons are used in the first layer, to have a good balance between the model accuracy and the model complexity. Experiments show that the the number of neurons

_{Q}*N*needed to accurately model the system is about twice the number of modes in the system, i.e.,

_{Q}*N*≈ 2

_{Q}*N*.

### 5.3. Aberration correction

*N*+ 2 = 11 control signals

*u*(

*k*),

*k*= 1, ⋯ ,

*N*+ 2, at a rate of 50 Hz. Inputs

*u*(

*k*) are initialized as in Section 3.3. The amplitude of the simplex is selected as half of the operational range of the PDM. After the intensity

*y*(

*k*),

*k*= 1, ⋯ ,

*N*+ 2, are collected, the aberration is estimated by solving a NLLS optimization problem as in Eq. (14), using the function

*fmincon*in MATLAB Optimization Toolbox.

*fmincon*is used in our work because: (1) it is computationally very efficient and can be called in MATLAB very conveniently; (2) the convexity of

*J*(

*â*,

*x̂*) improves with more data points so that a local optimization algorithm like

*fmincon*may already be enough to get an accurate estimation

*â*and

*x̂. â*is constrained to be within [0, 1] during the estimation. As time keeps going, more data points are available and the aberration is estimated and corrected iteratively as in the MBAC+Simplex algorithm. After

*P*= 19 data points, the simplex algorithm (named as Simplex 1) is switched on. For a comparison, the intensity is also maximized by the simplex algorithm alone (Simplex 2). Simplex 1 and Simplex 2 are the same except that the initial guess for Simplex 1 comes from the MBAC algorithm, but the initial guess for Simplex 2 is zero. Both simplex algorithms stop after

*P̂*= 200 intensity measurements, when they have converged. The sampling intervals between the 11

*th*and the 19

*th*samples vary because of the computational time of the NLLS algorithm, as will be discussed later. After Simplex 1 is switched on, the sampling rate returns to 50 Hz.

*ỹ*(

*k*) =

*y*(

*k*)/(

*y** 0.78), where

_{max}*ỹ*(

*k*) is the normalized intensity and the intensity transmission ratio (78%) of the disturbance generator is accounted for. The initial intensity without correction is 0.17. After

*N*+ 2 = 11 samples are collected, the aberration is estimated and corrected by the MBAC algorithm. The intensity increases to 0.38 (about 2.2 times of the initial value) at the 12

*th*time sample. With one more data sample acquired, the intensity jumps to 0.83 at the 13

*th*time sample, which is almost 5 times of the initial value. At the 14

*th*time sample, the intensity already converges to 0.86 and the intensity keeps at about 0.86 from the 15

*th*and 19

*th*samples.

*th*to the 29

*th*time samples. The initial simplex of Simplex 1 is constructed around the input point which gave the highest intensity in the past 19 samples, as described at the end of Section 3.2. Since the initialization of the simplex algorithm is only for data collection, intensity fluctuation is observed from the 20

*th*to the 29

*th*time samples as expected. However, after the initialization of Simplex 1 is completed, the intensity is further improved by Simplex 1 as can be seen from the small plot in Fig. 6. This plot shows that Simplex 1 converges faster than Simplex 2 because the MBAC algorithm provides a better initial value for Simplex 1.

*ỹ*(

*k*) for

*k*≥ 12. The initial intensity is 0.49 in average. With the MBAC algorithm, the intensity increases to 0.82 (an improvement of 67%) and 0.87 (an improvement of 78%) at the 12

*th*and 13

*th*time sample, respectively. The intensity converges to 0.89 at the 15

*th*time sample, while it takes Simplex 2 about 45 time samples to reach the same level. Because Simplex 1 starts at a better initial condition provide by MBAC, the intensity reaches 0.95 at the 60

*th*time sample, while Simplex 2 takes 90 time samples to reach the same level. A significant improvement has been achieved in correction speed. The standard deviation of

*ỹ*(

*k*) with MBAC is also smaller than with the simplex algorithm. For instance, at the 15

*th*time sample, the standard deviation of

*ỹ*(

*k*) with the MBAC algorithm is about 0.02 while that with Simplex 2 is 0.08, about 3 times larger. This indicates that the MBAC algorithm can improve the intensity in a more deterministic manner than simplex.

### 5.4. Computational complexity

*J*(

*â*,

*x̂*) is evaluated for about 578 times by the function

*fmincon*and

*t*

_{c,1}is about 40 ms in average. The sampling interval between the 11

*th*and the 12

*th*time sample is then equal to

*T*

_{s,1}=

*t*

_{c,1}+

*t*= 40 + 20 = 60 ms. In the aberration estimations afterwards, because a better initial guess is provided for

_{s}*â*and

*x̂*, the number of cost function evaluations is reduced to 251 in average and the computational time

*t*

_{c}_{,2}reduces to about 20 ms. The sampling interval becomes

*T*

_{s,2}= 20 + 20 = 40 ms.

*t*× 11 +

_{s}*T*

_{s,1}+

*T*

_{s,2}× 3 = 400 ms, while the simplex algorithm alone needs 45 time samples (i.e.,

*t*× 45 = 900 ms) to reach the same intensity level. A reduction of 56% has been achieved in the correction time. If a higher intensity end value is desired, e.g., 0.95, simplex alone needs 90 time samples in average (i.e.,

_{s}*t*× 90 = 1.80 s). The hybrid MBAC+Simplex algorithm needs 60 time samples (19 time samples by MBAC and 41 by Simplex 1), which takes

_{s}*t*× 11+

_{s}*T*

_{s,1}+

*T*

_{s,2}× 7+

*t*× 41 = 1.24 s in average. The time needed by the MBAC+Simplex algorithm is only 70% of that by the simplex algorithm alone.

_{s}## 6. Conclusion

*N*+ 2 intensity measurements. Experimental results show that in average 82% of the maximum intensity can be achieved at the

*N*+ 3 = 12

*th*time sample by the MBAC algorithm and intensity converges to 89% at the 15

*th*time sample. With the better initial condition provided by the MBAC algorithm, the simplex algorithm also shows faster convergence than used alone.

## Acknowledgments

## References and links

1. | M. A. Vorontsov, G. W. Carhart, D. V. Pruidze, J. C. Ricklin, and D. G. Voelz, “Adaptive imaging system for phase-distorted extended source and multiple-distance objects,” Appl. Opt. |

2. | G. Vdovin, “Optimization-based operation of micromachined deformable mirrors,” Proc. SPIE |

3. | M. A. Vorontsov, G. W. Carhart, M. Cohen, and G. Cauwenberghs, “Adaptive optics based on analog parallel stochastic optimization: analysis and experimental demonstration,” J. Opt. Soc. Am. A |

4. | W. Lubeigt, G. Valentine, J. M. Girkin, E. Bente, and D. Burns, “Active transverse mode control and optimization of an all-solid-state laser using an intracavity adaptive-optic mirror,” Opt. Express |

5. | U. Wittrock, I. Buske, and H. M. Heuck, “Adaptive aberration control in laser amplifiers and laser resonators,” Proc. SPIE |

6. | M. de Boer, K. Hinnen, M. Verhaegen, R. Fraanje, G. Vdovin, and N. Doelman, “Control of a thermal deformable mirror: correction of a static disturbance with limited sensor information,” in Proceedings of the 4th International Workshop on Adaptive Optics for Industry and Medicine, pages 61–71, Münster, Germany, 2003. |

7. | R. El-Agmy, H. Bulte, A. H. Greenaway, and D. Reid, “Adaptive beam profile control using a simulated annealing algorithm,” Opt. Express |

8. | A. A. Aleksandrov, A. V. Kudryashov, A. L. Rukosuev, T. Yu. Cherezova, and Yu. V. Sheldakova, “An adaptive optical system for controlling laser radiation,” J. Opt. Technol. |

9. | P. Yang, Y. Liu, W. Yang, M. W. Ao, S. J. Hu, B. Xu, and W. H. Jiang, “Adaptive mode optimization of a continuous-wave solid-state laser using an intracavity piezoelectric deformable mirror,” Opt. Commun. |

10. | W. Lubeigt, S. P. Poland, G. J. Valentine, A. J. Wright, J. M. Girkin, and D. Burns, “Search-based active optic systems for aberration correction in time-independent applications,” Appl. Opt. |

11. | O. Albert, L. Sherman, G. Mourou, T. B. Norris, and G. Vdovin, “Smart microscope: an adaptive optics learning system for aberration correction in multiphoton confocal microscopy. Opt. Lett. , |

12. | L. Sherman, J. Y. Ye, O. Albert, and T. B. Norris, “Adaptive correction of depth-induced aberrations in multiphoton scanning microscopy using a deformable mirror,” J. Microsc. |

13. | M. J. Booth, M. A. A. Neil, R. Juskaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Nat. Acad. Sci. U.S.A. |

14. | P. Marsh, D. Burns, and J. M. Girkin, “Practical implementation of adaptive optics in multiphoton microscopy,” Opt. Express |

15. | A. J. Wright, D. Burns, B. A. Patterson, S. P. Poland, G. J. Valentine, and J. M. Girkin, “Exploration of the optimisation algorithms used in the implementation of adaptive optics in confocal and multiphoton microscopy,” Microsc. Res. Tech. |

16. | S. P. Poland, A. J. Wright, and J. M. Girkin, “Evaluation of fitness parameters used in an iterative approach to aberration correction in optical sectioning microscopy,” Appl. Opt. |

17. | D. Débarre, E. J. Botcherby, M. J. Booth, and T. Wilson, “Adaptive optics for structured illumination microscopy,” Opt. Express |

18. | D. Débarre, E. J. Botcherby, T. Watanabe, S. Srinivas, M. J. Booth, and T. Wilson, “Image-based adaptive optics for two-photon microscopy,” Opt. Lett. |

19. | F. Roddier, |

20. | J. W. Hardy, |

21. | M. J. Booth, “Wave front sensor-less adaptive optics: a model-based approach using sphere packings,” Opt. Express |

22. | J. W. Goodman, |

23. | H. Song, G. Vdovin, R. Fraanje, G. Schitter, and M. Verhaegen, “Extracting hysteresis from nonlinear measurement of wavefront-sensorless adaptive optics system,” Opt. Lett. |

24. | M. Verhaegen and V. Verdult, |

25. | J. Sjöberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P. Glorennec, H. Hjalmarsson, and A. Juditsky, “Non-linear black-box modeling in system identification: a unified overview,” |

26. | M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high NA adaptive optical microscopy,” Opt. Express |

27. | G. Vdovin, O. Soloviev, A. Samokhin, and M. Loktev, “Correction of low order aberrations using continuous deformable mirrors,” Opt. Express |

28. | S. Y. Kung, |

29. | S. Haykin, |

30. | M. Brown and C. Harris, |

31. | H. Demuth, M. Beale, and M. Hagan, |

32. | M. Born and E. Wolf, |

33. | W. H. Press, S. A. Teukolsky, and W. T. Vetterling, |

34. | M. Loktev, D. Monteiroa, and G. Vdovin, “Comparison study of the performance of piston, thin plate and membrane mirrors for correction of turbulence-induced phase distortions,” Opt. Commun. |

**OCIS Codes**

(010.1080) Atmospheric and oceanic optics : Active or adaptive optics

(010.7350) Atmospheric and oceanic optics : Wave-front sensing

(220.1000) Optical design and fabrication : Aberration compensation

(110.0113) Imaging systems : Imaging through turbid media

**ToC Category:**

Active and Adaptive Optics

**History**

Original Manuscript: July 15, 2010

Manuscript Accepted: August 23, 2010

Published: November 3, 2010

**Virtual Issues**

Vol. 6, Iss. 1 *Virtual Journal for Biomedical Optics*

**Citation**

H. Song, R. Fraanje, G. Schitter, H. Kroese, G. Vdovin, and M. Verhaegen, "Model-based aberration correction in a
closed-loop wavefront-sensor-less
adaptive optics system," Opt. Express **18**, 24070-24084 (2010)

http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-23-24070

Sort: Year | Journal | Reset

### References

- M. A. Vorontsov, G. W. Carhart, D. V. Pruidze, J. C. Ricklin, and D. G. Voelz, "Adaptive imaging system for phase-distorted extended source and multiple-distance objects," Appl. Opt. 36(15), 3319-3328 (1997). [CrossRef] [PubMed]
- G. Vdovin, "Optimization-based operation of micromachined deformable mirrors," Proc. SPIE 3353, 902-909 (1998). [CrossRef]
- M. A. Vorontsov, G. W. Carhart, M. Cohen, and G. Cauwenberghs, "Adaptive optics based on analog parallel stochastic optimization: analysis and experimental demonstration," J. Opt. Soc. Am. A 17(8), 1440-1453 (2000). [CrossRef]
- W. Lubeigt, G. Valentine, J. M. Girkin, E. Bente, and D. Burns, "Active transverse mode control and optimization of an all-solid-state laser using an intracavity adaptive-optic mirror," Opt. Express 10(13), 550-555 (2002). [PubMed]
- U. Wittrock, I. Buske, and H. M. Heuck, "Adaptive aberration control in laser amplifiers and laser resonators," Proc. SPIE 4969, 122-136 (2003). [CrossRef]
- M. de Boer, K. Hinnen, M. Verhaegen, R. Fraanje, G. Vdovin, and N. Doelman, "Control of a thermal deformable mirror: correction of a static disturbance with limited sensor information," in Proceedings of the 4th International Workshop on Adaptive Optics for Industry and Medicine, pages 61-71, Münster, Germany, 2003.
- R. El-Agmy, H. Bulte, A. H. Greenaway, and D. Reid, "Adaptive beam profile control using a simulated annealing algorithm," Opt. Express 13(16), 6085-6091 (2005). [CrossRef] [PubMed]
- A. A. Aleksandrov, A. V. Kudryashov, A. L. Rukosuev, T. Yu. Cherezova, and Yu. V. Sheldakova, "An adaptive optical system for controlling laser radiation," J. Opt. Technol. 74(8), 550-554 (2007). [CrossRef]
- P. Yang, Y. Liu, W. Yang, M. W. Ao, S. J. Hu, B. Xu, and W. H. Jiang, "Adaptive mode optimization of a continuous-wave solid-state laser using an intracavity piezoelectric deformable mirror," Opt. Commun. 278(2), 377-381 (2007). [CrossRef]
- W. Lubeigt, S. P. Poland, G. J. Valentine, A. J. Wright, J. M. Girkin, and D. Burns, "Search-based active optic systems for aberration correction in time-independent applications," Appl. Opt. 49(3), 307-314 (2010). [CrossRef] [PubMed]
- O. Albert, L. Sherman, G. Mourou, T. B. Norris, and G. Vdovin, "Smart microscope: an adaptive optics learning system for aberration correction in multiphoton confocal microscopy," Opt. Lett. 25(1), 52-54 (2000). [CrossRef]
- L. Sherman, J. Y. Ye, O. Albert, and T. B. Norris, "Adaptive correction of depth-induced aberrations in multiphoton scanning microscopy using a deformable mirror," J. Microsc. 206(1), 65-71 (2002). [CrossRef] [PubMed]
- M. J. Booth, M. A. A. Neil, R. Juskaitis, and T. Wilson, "Adaptive aberration correction in a confocal microscope," Proc. Natl. Acad. Sci. U.S.A. 99(9), 5788-5792 (2002). [CrossRef]
- P. Marsh, D. Burns, and J. M. Girkin, "Practical implementation of adaptive optics in multiphoton microscopy," Opt. Express 11(10), 1123-1130 (2003). [CrossRef] [PubMed]
- A. J. Wright, D. Burns, B. A. Patterson, S. P. Poland, G. J. Valentine, and J. M. Girkin, "Exploration of the optimisation algorithms used in the implementation of adaptive optics in confocal and multiphoton microscopy," Microsc. Res. Tech. 67(1), 36-44 (2005). [CrossRef] [PubMed]
- S. P. Poland, A. J. Wright, and J. M. Girkin, "Evaluation of fitness parameters used in an iterative approach to aberration correction in optical sectioning microscopy," Appl. Opt. 47(6), 731-736 (2008). [CrossRef] [PubMed]
- D. Débarre, E. J. Botcherby, M. J. Booth, and T. Wilson, "Adaptive optics for structured illumination microscopy," Opt. Express 16(13), 9290-9305 (2008). [CrossRef] [PubMed]
- D. Débarre, E. J. Botcherby, T. Watanabe, S. Srinivas, M. J. Booth, and T. Wilson, "Image-based adaptive optics for two-photon microscopy," Opt. Lett. 34(16), 2495-2497 (2009). [CrossRef] [PubMed]
- F. Roddier, Adaptive Optics in Astronomy, (Cambridge University Press, Cambridge, UK, 1999). [CrossRef]
- J. W. Hardy, Adaptive Optics for Astronomical Telescopes, (Oxford University Press, New York, USA, 1998).
- M. J. Booth, "Wave front sensor-less adaptive optics: a model-based approach using sphere packings," Opt. Express 14(4), 1339-1352 (2006). [CrossRef] [PubMed]
- J. W. Goodman, Introduction to Fourier Optics, 2nd ed. (McGraw-Hill, USA, 1996).
- H. Song, G. Vdovin, R. Fraanje, G. Schitter, and M. Verhaegen, "Extracting hysteresis from nonlinear measurement of wavefront-sensorless adaptive optics system," Opt. Lett. 34(1), 61-63 (2009). [CrossRef]
- M. Verhaegen, and V. Verdult, Filtering and System Identification: A Least Squares Approach, (Cambridge University Press, Cambridge, USA, 2007). [CrossRef]
- J. Sjöberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P. Glorennec, H. Hjalmarsson, and A. Juditsky, "Nonlinear black-box modeling in system identification: a unified overview," Automatica 31(12), 1691-1724 (1995). [CrossRef]
- M. Schwertner, M. J. Booth, and T. Wilson, "Characterizing specimen induced aberrations for high NA adaptive optical microscopy," Opt. Express 12(26), 6540-6552 (2004). [CrossRef] [PubMed]
- G. Vdovin, O. Soloviev, A. Samokhin, and M. Loktev, "Correction of low order aberrations using continuous deformable mirrors," Opt. Express 16(5), 2859-2866 (2008). [CrossRef] [PubMed]
- S. Y. Kung, Digital Neural Networks, (Prentice-Hall, Upper Saddle River, NJ, USA, 1993).
- S. Haykin, Neural Networks: a Comprehensive Foundation, (Macmillan, New York, USA, 1994).
- M. Brown, and C. Harris, Neurofuzzy Adaptive Modeling and Control, (Prentice-Hall, New York, USA, 1994).
- H. Demuth, M. Beale, and M. Hagan, Neural Network Toolbox 5 User’s Guide, (The MathWorks, Inc., 2007). [PubMed]
- M. Born, and E. Wolf, Principles of Optics: Electromagnetic Theory of Propagation, 7th ed. (Cambridge University Press, Cambridge, UK, 1999).
- W. H. Press, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C; the Art of Scientific Computing, 2nd ed. (Cambridge University Press, New York, USA, 1992).
- M. Loktev, D. Monteiroa, and G. Vdovin, "Comparison study of the performance of piston, thin plate and membrane mirrors for correction of turbulence-induced phase distortions," Opt. Commun. 192, 91-99 (2001). [CrossRef]

## Cited By |
Alert me when this paper is cited |

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article | Next Article »

OSA is a member of CrossRef.