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Multicriteria second‐order neural networks approach to imaging through turbulence
Author(s) -
Wang Yuanmei
Publication year - 2003
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.10037
Subject(s) - turbulence , atmospheric turbulence , computer science , speckle pattern , artificial neural network , entropy (arrow of time) , convolution (computer science) , image resolution , algorithm , artificial intelligence , physics , meteorology , quantum mechanics
Atmospheric turbulence can greatly limit the spatial resolution in optical images obtained of space objects when imaged with ground‐based telescopes. Two widely used algorithms to remove atmospheric turbulence in this class of images are blind de‐convolution and speckle imaging. Both algorithms are effective in removing atmospheric turbulence, but they use different types of prior knowledge and have different strengths and weaknesses. We have developed a multicriteria cross entropy minimization approach to imaging through atmospheric turbulence and a second‐order neural network implementations. Our simulations illustrated the efficiency of our method. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 146–151, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10037