2022
DOI: 10.1364/ao.461222
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Deviation-based wavefront correction using the SPGD algorithm for high-resolution optical remote sensing

Abstract: Model-free image-based wavefront correction techniques, such as the stochastic parallel gradient descent (SPGD) algorithm, will be useful in achieving diffraction-limited optical performance in near-future optical remote sensing systems. One difficulty facing the image-based method is that the correction performance depends on the evaluation metric and the evaluated scene. We propose several evaluation functions and investigate the relationship between the optimization speed and the scene textures for each met… Show more

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Cited by 9 publications
(2 citation statements)
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“…Figure 6 shows the images obtained before and after stacking the segment images (a and b) and final optimized PSF (c). The optimization of the adjustment was performed by stochastic parallel gradient descent (SPGD) algorithm [7,8].…”
Section: Preliminary Testingmentioning
confidence: 99%
“…Figure 6 shows the images obtained before and after stacking the segment images (a and b) and final optimized PSF (c). The optimization of the adjustment was performed by stochastic parallel gradient descent (SPGD) algorithm [7,8].…”
Section: Preliminary Testingmentioning
confidence: 99%
“…( 10) gets a maximum modified gradient. The validity of the iterative method is assured by: (i) both the delayed phase and the aberrations induced by the turbulence is composed of several low-order modes, thus can be compensated by a optimized φ c n ( r 0 ) [30]. (ii) G( r r ) will not feature maximum gradient until both the delayed phase and the aberrations caused by the turbulence are compensated [31] by a optimized φ c n ( r 0 ).…”
Section: Principlesmentioning
confidence: 99%