Literature DB >> 19516823

Wavefront reconstruction with artificial neural networks.

Hong Guo, Nina Korablinova, Qiushi Ren, Josef Bille.   

Abstract

In this work, a new approach, a method using artificial neural networks was applied to reconstruct the wavefront. First, the optimal structure of neural networks was found. Then, the networks were trained on both noise-free and noisy spot patterns. The results of the wavefront reconstruction with artificial neural networks were compared to those obtained through the least square fit and singular value decomposition method.

Year:  2006        PMID: 19516823     DOI: 10.1364/oe.14.006456

Source DB:  PubMed          Journal:  Opt Express        ISSN: 1094-4087            Impact factor:   3.894


  3 in total

1.  Accurate phase retrieval of complex 3D point spread functions with deep residual neural networks.

Authors:  Leonhard Möckl; Petar N Petrov; W E Moerner
Journal:  Appl Phys Lett       Date:  2019-12-18       Impact factor: 3.791

2.  Simplifying the Experimental Detection of the Vortex Topological Charge Based on the Simultaneous Astigmatic Transformation of Several Types and Levels in the Same Focal Plane.

Authors:  Pavel A Khorin; Svetlana N Khonina; Alexey P Porfirev; Nikolay L Kazanskiy
Journal:  Sensors (Basel)       Date:  2022-09-28       Impact factor: 3.847

3.  A Method Used to Improve the Dynamic Range of Shack-Hartmann Wavefront Sensor in Presence of Large Aberration.

Authors:  Wen Yang; Jianli Wang; Bin Wang
Journal:  Sensors (Basel)       Date:  2022-09-20       Impact factor: 3.847

  3 in total

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