Real time phase diversity advanced image processing and wavefront sensing

Dolne, Jean J.; Menicucci, Paul; Miccolis, David; Widen, Ken; Seiden, Harold; Vachss, Frederick; Schall, H.

doi:10.1117/12.740064

Cited by 4 publications

(3 citation statements)

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“…When compared to SHWFS and SI, the phase diversity (PD) method proposed in [ 6 ] has a simpler optical path and no non-common optical path aberration [ 7 , 8 ]. However, due to high computational complexity, the PD method is mainly applied in the field of post-processing of blurred image and areas with lower real-time requirements [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Georges III et al [ 14 ] proposed a proof-of-concept phase-diversity wavefront sensing and control testbed that displayed

wave Root Mean Square accuracy, operated at an estimation rate of 100 Hz. Dolne et al [ 10 ] proposed an approach for real-time wavefront sensing and image enhancement that could process PD images at 50 to 200 Hz. Miyamura et al [ 15 ] also used a neural network to solve the complicated inverse problem of the PD method.…”

Section: Introductionmentioning

confidence: 99%

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Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor

Guo

Bao

et al. 2020

Sensors

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We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about 0.5 ms, while fusing the information of the focal and defocused intensity images. When compared to the traditional phase diversity (PD) algorithms, the PD-CNN is a light-weight model without complicated iterative transformation and optimization process. Experiments have been done to demonstrate the accuracy and speed of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

“…Georges III et al [ 14 ] proposed a proof-of-concept phase-diversity wavefront sensing and control testbed that displayed

Section: Introductionmentioning

confidence: 99%

Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor

Guo

Bao

et al. 2020

Sensors

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“…Research efforts are looking at different ways to use this type of WFS technique in a real-time adaptive optics system. Demonstrations of real-time correction have been obtained for very few corrected aberrations 6 . Improvements to PD have been made by better numerical algorithms 7,8 , by using object independent error metrics to estimate the aberrations from the data 9 or by different linearization techniques [10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

Bilinear solution to the phase diversity problem for extended objects based on the Born approximation

et al. 2012

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We propose a new approach for the joint estimation of aberration parameters and unknown object from diversity images with applications in imaging systems with extended objects as astronomical ground-based observations or solar telescopes. The motivation behind our idea is to decrease the computational complexity of the conventional phase diversity (PD) algorithm and avoid the convergence to local minima due to the use of nonlinear estimation algorithms. Our approach is able to give a good starting point for an iterative algorithm or it can be used as a new wavefront estimation method. When the wavefront aberrations are small, the wavefront can be approximated with a linear term which leads to a quadratic point-spread function (PSF) in the aberration parameters. The presented approach involves recording two or more diversity images and, based on the before mentioned approximation estimates the aberration parameters and the object by solving a system of bilinear equations, which is obtained by subtracting from each diversity image the focal plane image. Moreover, using the quadratic PSFs gives improved performance to the conventional PD algorithm through the fact that the gradients of the PSFs have simple analytical formulas.

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