Multicriteria second-order neural networks approach to imaging through turbulence
✍ Scribed by Yuanmei Wang
- Publisher
- John Wiley and Sons
- Year
- 2003
- Tongue
- English
- Weight
- 130 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0899-9457
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✦ Synopsis
Abstract
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