Paper
10 May 2019 Quality evaluation for adaptive optical image through a logarithmic deviation of Rényi entropy
Author Affiliations +
Proceedings Volume 11068, Second Symposium on Novel Technology of X-Ray Imaging; 1106828 (2019) https://doi.org/10.1117/12.2524692
Event: Second Symposium on Novel Technology of X-Ray Imaging, 2018, Hefei, China
Abstract
The adaptive optical telescopes play a more and more important role in astronomy and satellite detection, and the space target images are so many that we need find a suitable method of quality evaluation to choose good quality images automatically in order to study the restorate image. It is well known that the adaptive optical images are no-reference images. In this paper, a new evaluation method based on the use of the logarithmic standard deviation of Rényi Entropy for the adaptive optical images is proposed. Through the discrete cosine transform using one dimension window, the statistical property of Rényi entropy for images is studied. The different directional Rényi entropy maps of an input image containing different information content are obtained. The mean values of different directional Rényi entropy maps are calculated. For image quality evaluation, the different directional Rényi entropy and its standard deviation corresponding to region of interest is selected as an indicator for the anisotropy of the images. In this paper, we define a logarithmic measure the standard deviation based on Rényi entropy can be selected as an indicator of quality evaluation for adaptive optical images. Experimental results show the proposed method that the sorting quality matches well with the visual inspection.
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Yuannan Xu, Junpeng Hui, Ze Wang , Hongjun Liu, Yunfo Liu , and Yu Cai "Quality evaluation for adaptive optical image through a logarithmic deviation of Rényi entropy", Proc. SPIE 11068, Second Symposium on Novel Technology of X-Ray Imaging, 1106828 (10 May 2019); https://doi.org/10.1117/12.2524692
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KEYWORDS
Image quality

Adaptive optics

Anisotropy

Image analysis

Image information entropy

Satellite imaging

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