1994
DOI: 10.1364/ao.33.006533
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Phase-diversity wave-front sensor for imaging systems

Abstract: A phase-diversity wave-front sensor has been developed and tested at the Lockheed Palo Alto Research Labs (LPARL). The sensor consists of two CCD-array focal planes that record the best-focus image of an adaptive imaging system and an image that is defocused. This information is used to generate an object-independent function that is the input to a LPARL-developed neural network algorithm known as the General Regression Neural Network (GRNN). The GRNN algorithm calculates the wave-front errors that are present… Show more

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Cited by 92 publications
(36 citation statements)
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“…The first one, which is called the standard image I These two images are used to compute a phase criterion χ ref that does not depend on the target intensity distribution and is a relevant signature of the actual piston errors. Kendrick et al (1994) proposed four different metrics to define the χ criterion…”
Section: Phase Diversitymentioning
confidence: 99%
“…The first one, which is called the standard image I These two images are used to compute a phase criterion χ ref that does not depend on the target intensity distribution and is a relevant signature of the actual piston errors. Kendrick et al (1994) proposed four different metrics to define the χ criterion…”
Section: Phase Diversitymentioning
confidence: 99%
“…Some algorithmic developments were leaded in parallel to optimize the noise reduction [15] or to reduce computation time for cophasing applications [16]. To be exhaustive, we should mention some tests with phase diversity in real time control of an adaptive optics system on extended scene for astronomy [17]. The RASCASSE project have been thought as a whole study gathering numerical simulations and experience of these two wave-front sensors: Shack-Hartmann, and phase diversity.…”
Section: I2 State-of-the Art Of the Wave-front Sensors For Space Appmentioning
confidence: 99%
“…PCA is used for extracting {p k }, which is described in section 3.2. For the architecture of the learning system, the generalized regression neural network (GRNN), of which the response function of neurons consists of a radial basis function, is used for the estimation of aberration parameters 12) . By means of GRNN, a is represented as a function of {p k }.…”
Section: Estimation Of Aberration Parameters From Observed Imagesmentioning
confidence: 99%