Literature DB >> 15008490

Wave-front reconstruction from shear phase maps by use of the discrete Fourier transform.

Alfredo Dubra1, Carl Paterson, Christopher Dainty.   

Abstract

Fast wave-front reconstruction methods are becoming increasingly important, for example, in large astronomical adaptive optics systems and high spatial resolution shear interferometry, where pseudoinverse matrix methods scale poorly with problem size. Wave-front reconstruction from difference measurements can be achieved by use of fast implementations of the discrete Fourier transform (DFT), obtaining performance comparable with that of the pseudoinverse in terms of the noise propagation coefficient. Existing methods that are based on the use of the DFT give exact results (in the absence of noise) only for the particular case in which the shear is a divisor of the number of samples to be reconstructed. We present two alternate solutions for the more general case when the shear is any integer. In the first solution the dimensions of the problem are enlarged, and in the second the problem is subdivided into a set of smaller problems with shear amplitude equal to one. We also show that the retrieved solutions have minimum norm and calculate the noise propagation coefficient for both methods. The proposed algorithms are implemented and timed against pseudoinverse multiplication. The results show a speed increase by a factor of 50 over the pseudoinverse multiplication for a grid with N = 3 x 10(3) samples.

Year:  2004        PMID: 15008490     DOI: 10.1364/ao.43.001108

Source DB:  PubMed          Journal:  Appl Opt        ISSN: 1559-128X            Impact factor:   1.980


  4 in total

1.  Wavefront sensor and wavefront corrector matching in adaptive optics.

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Journal:  Opt Express       Date:  2007-03-19       Impact factor: 3.894

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Journal:  Biomed Opt Express       Date:  2021-01-08       Impact factor: 3.732

3.  Quadriwave lateral shearing interferometric microscopy with wideband sensitivity enhancement for quantitative phase imaging in real time.

Authors:  Tong Ling; Jiabin Jiang; Rui Zhang; Yongying Yang
Journal:  Sci Rep       Date:  2017-01-31       Impact factor: 4.379

4.  ECG data compression using a neural network model based on multi-objective optimization.

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Journal:  PLoS One       Date:  2017-10-03       Impact factor: 3.240

  4 in total

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