Infrared traffic image's enhancement algorithm combining dark channel prior and Gamma correction
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摘要: 为了有效提高智能交通监控设备采集到的红外交通图像的视觉质量, 将可见光图像去雾的方法引入红外交通图像的增强处理过程中, 提出一种结合暗原色优先和Gamma校正的红外交通图像增强新算法, 首先采用暗原色优先算法对原始降质红外交通图像进行处理而得到初步增强的图像, 然后使用Gamma校正算法对初步增强的图像亮度进行调节, 并将新算法与其他常见的红外图像增强算法进行图像增强效果的对比分析。试验结果表明: 两幅原始红外交通图像的信息熵分别为4.71、5.07, 经过新算法处理后信息熵分别增加到6.45、5.92;两幅原始红外交通图像的灰度标准差分别为6.90、19.14, 经过新算法处理后灰度标准差分别增加到31.17、32.35;新算法的信息熵计算值大于他他算法的计算值。可见, 新算法的增强效果优于其他常见的红外图像增强算法, 它能显著改善红外交通图像的视觉效果, 为图像的后续处理与分析奠定良好的基础。Abstract: In order to enhance the visual quality of infrared traffic image collected by the intelligent traffic monitoring equipment effectively, the image defogging method of visible light was introduced into traffic infrared image enhancement processing, and a new infrared traffic image's enhancement algorithm combining dark channel prior and Gamma correction was proposed.First, the original degraded infrared traffic image was processed by dark channel prior algorithm to obtain initially enhanced image.Then, the brightness of initially enhanced image was adjusted by Gamma correction algorithm.The image enhancement effects of the new algorithm and other common infrared image enhancement algorithms were compared.Test result shows that the information entropies of two original infrared traffic images are respectively 4.71 and 5.07 and respectively increase to 6.45 and 5.92 after being processed by the new algorithm.The standard deviations of gray scale for two original infrared traffic images are respectively 6.90 and 19.14 and respectively increase to 31.17 and 32.35 after being processed by the new algorithm.The information entropy computational value of new algorithm is more than the values of other algorithms.So the enhancement effect of the proposed algorithm is better than the enhancement effects of other common infrared image enhancement algorithms, and it can significantly improve the visual effect of infrared traffic image and lay good foundation for following processing and analysis of image.
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Key words:
- image processing /
- infrared traffic image /
- image defogging /
- image enhancement /
- dark channel prior /
- Gamma correction
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图 10 原始红外交通图像1的灰度直方图
Figure 10. Gray scale histogram of original infrared traffic image 1
图 11 算法1处理后的图像1的灰度直方图
Figure 11. Gray scale histogram of image 1processed by algorithm 1
图 12 算法2处理后的图像1的灰度直方图
Figure 12. Gray scale histogram of image 1processed by algorithm 2
图 13 算法3处理后的图像1的灰度直方图
Figure 13. Gray scale histogram of image 1processed by algorithm 3
图 14 算法4处理后的图像1的灰度直方图
Figure 14. Gray scale histogram of image 1processed by algorithm 4
图 15 算法5处理后的图像1的灰度直方图
Figure 15. Gray scale histogram of image 1processed by algorithm 5
图 22 原始红外交通图像2的灰度直方图
Figure 22. Gray scale histogram of original infrared traffic image 2
图 23 算法1处理后的图像2的灰度直方图
Figure 23. Gray scale histogram of image 2processed by algorithm 1
图 24 算法2处理后的图像2的灰度直方图
Figure 24. Gray scale histogram of image 2processed by algorithm 2
图 25 算法3处理后的图像2的灰度直方图
Figure 25. Gray scale histogram of image 2processed by algorithm 3
图 26 算法4处理后的图像2的灰度直方图
Figure 26. Gray scale histogram of image 2processed by algorithm 4
图 27 算法5处理后的图像2的灰度直方图
Figure 27. Gray scale histogram of image 2processed by algorithm 5
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