• Chinese Journal of Lasers
  • Vol. 41, Issue 12, 1209002 (2014)
Chen Ying1、2、*, Zhu Ming1, and Li Zhaoze3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3788/cjl201441.1209002 Cite this Article Set citation alerts
    Chen Ying, Zhu Ming, Li Zhaoze. Remote Sensing Digital Image Enhancement Based on Gaussian Mixture Modeling[J]. Chinese Journal of Lasers, 2014, 41(12): 1209002 Copy Citation Text show less

    Abstract

    The remote sensing image is susceptible to the clouds and fog. In order to improve the output quality of low contrast image and maintain the details, an image enhancement algorithm based on Gaussian mixture modeling (GMM) is proposed. The histogram of original image is smoothed with a 1×3 filter. The best parameters of GMM is got by fitting the histogram with the expectation maximization (EM) algorithm, and the histogram is separated into sub-histograms based on the optimal intersections. The mapping of output image is got according to the Gaussian parameters, and the final enhanced image is obtained. Results of experiments show that the algorithm can determine the optimal number of clusters adaptively and improve the speed of the histogram fitting which costs 0.37 s averagely. Comparing with traditional methods, the enhancement result is superior in terms of objective evaluations of related information entropy and texture information. It can improve the contrast of the remote sensing image while maintaining the details effectively.
    Chen Ying, Zhu Ming, Li Zhaoze. Remote Sensing Digital Image Enhancement Based on Gaussian Mixture Modeling[J]. Chinese Journal of Lasers, 2014, 41(12): 1209002
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