Dr. Yuannan Xu Profile

Dr. Yuannan Xu

at 207suo

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Publications (6)

Proceedings Article | 10 May 2019 Paper

Quality evaluation for adaptive optical image through a logarithmic deviation of Rényi entropy

Yuannan Xu, Junpeng Hui, Ze Wang , Hongjun Liu, Yunfo Liu , Yu Cai

Proceedings Volume 11068, 1106828 (2019) https://doi.org/10.1117/12.2524692

KEYWORDS: Image quality, Adaptive optics, Anisotropy, Image analysis, Satellite imaging, Image information entropy

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Proceedings Article | 26 January 2016 Paper

Object-oriented recognition of high-resolution remote sensing image

Yongyan Wang, Haitao Li, Hong Chen, Yuannan Xu

Proceedings Volume 9796, 97962W (2016) https://doi.org/10.1117/12.2230491

KEYWORDS: Image segmentation, Remote sensing, Detection and tracking algorithms, Target recognition, Image processing, Image resolution, Satellite imaging, Satellites, Image processing algorithms and systems, Spectral resolution

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Proceedings Article | 26 January 2016 Paper

Ground target detection based on discrete cosine transform and Rényi entropy for imaging ladar

Yuannan Xu, Weili Chen, Junwei Li, Yanbing Dong

Proceedings Volume 9796, 97962L (2016) https://doi.org/10.1117/12.2229387

KEYWORDS: Computing systems, Target detection, LIDAR, Image fusion, Image segmentation, Data analysis, Human vision and color perception, Statistical analysis, Visual system, Cognition

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Proceedings Article | 13 April 2015 Paper

Quality evaluation of adaptive optical image based on DCT and Rényi entropy

Yuannan Xu, Junwei Li, Jing Wang, Rong Deng, Yanbing Dong

Proceedings Volume 9522, 95222Z (2015) https://doi.org/10.1117/12.2182768

KEYWORDS: Image quality, Adaptive optics, Anisotropy, Image analysis, Optical inspection, Coherence (optics), Optical telescopes, Image information entropy, Turbulence, Signal to noise ratio

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Proceedings Article | 26 October 2013 Paper

Ladar imaging detection of salient map based on PWVD and Rényi entropy

Yuannan Xu, Yuan Zhao, Rong Deng, Yanbing Dong

Proceedings Volume 8917, 891716 (2013) https://doi.org/10.1117/12.2032430

KEYWORDS: Target detection, LIDAR, Image segmentation, Feature extraction, Human vision and color perception, Statistical analysis, Visual system, Cognition, Computing systems, Machine vision

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Proceedings Article | 19 September 2013 Paper

The simulation of adaptive optical image even and pulse noise and research of image quality evaluation

Changli Wen, Yuannan Xu, Rong Xu, Changhai Liu, Tao Men, Wei Niu

Proceedings Volume 8905, 890508 (2013) https://doi.org/10.1117/12.2031727

KEYWORDS: Image quality, Image analysis, Adaptive optics, Error analysis, Optical telescopes, Optical simulations, Space telescopes, Astronomical engineering, Satellite imaging, Satellites

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As optical image becomes more and more important in adaptive optics area, and adaptive optical telescopes play a more and more important role in the detection system on the ground, and the images we get are so many that we need find a suitable method to choose good quality images automatically in order to save human power, people pay more and more attention in image’s evaluation methods and their characteristics. According to different image degradation model, the applicability of different image’s quality evaluation method will be different. Researchers have paid most attention in how to improve or build new method to evaluate degraded images. Now we should change our way to take some research in the models of degradation of images, the reasons of image degradation, and the relations among different degraded images and different image quality evaluation methods. In this paper, we build models of even noise and pulse noise based on their definition and get degraded images using these models, and we take research in six kinds of usual image quality evaluation methods such as square error method, sum of multi-power of grey scale method, entropy method, Fisher function method, Sobel method, and sum of grads method, and we make computer software for these methods to use easily to evaluate all kinds of images input. Then we evaluate the images’ qualities with different evaluation methods and analyze the results of six kinds of methods, and finally we get many important results. Such as the characteristics of every method for evaluating qualities of degraded images of even noise, the characteristics of every method for evaluating qualities of degraded images of pulse noise, and the best method to evaluate images which affected by tow kinds of noise both and the characteristics of this method. These results are important to image’s choosing automatically, and this will help we to manage the images we get through adaptive optical telescopes base on the ground.

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