Multicriteria second-order neural networks approach to imaging through turbulence

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