Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (5): 1427-1431.DOI: 10.11772/j.issn.1001-9081.2017102480

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Foggy image enhancement based on adaptive Riesz fractional differential

LEI Sijia, ZHAO Fengqun   

  1. School of Sciences, Xi'an University of Technology, Xi'an Shaanxi 710054, China
  • Received:2017-10-20 Revised:2017-12-18 Online:2018-05-10 Published:2018-05-24
  • Contact: 雷思佳
  • Supported by:
    This work is partially supported by the Industrial Science and Technology Project of Shaanxi Province (2015GY004).

基于自适应Riesz分数阶微分的雾天图像增强

雷思佳, 赵凤群   

  1. 西安理工大学 理学院, 西安 710054
  • 通讯作者: 雷思佳
  • 作者简介:雷思佳(1992-),女,陕西延安人,硕士研究生,主要研究方向:图像处理与分析;赵凤群(1963-),女,陕西咸阳人,教授,博士,主要研究方向:图像处理与分析、微分方程数值解。
  • 基金资助:
    陕西省工业科技攻关项目(2015GY004)。

Abstract: In order to improve clarity of foggy images and solve the problem of unicity of fractional order, a new adaptive fractional differential image enhancement method was proposed. Based on an approximate formula of Riesz fractional differential with six order accuracy, a new high precision fractional differential mask:RH operator (Riesz Higher order operator) was constructed, and the IRH operator (Improved Riesz Higher order operator) was proposed by improving RH operator. A fractional differential function was established based on image local features, and a selection criterion of fractional differential order was proposed, and then the adaptive selection method of order point by point was implemented. Combining with IRH operator, an adaptive fractional differential image enhancement algorithm was formed. For color images, due to low independence among components in RGB space, color distortion may occur after enhancement of each channel. Therefore, the image was converted from the RGB space to the HSV space and only the luminance channel was enhanced. A group of foggy images was selected compared with Tiansi operator, the segmentation-based adaptive fractional differential image enhancement algorithm and the adaptive fractional-differential compound bilateral filtering algorithm. The results show that the proposed method has obvious enhancement effect by calculating the information entropy and average gradient in comparison with methods in the reference, which further demonstrates the effectiveness of the proposed algorithm.

Key words: Riesz fractional differential, image enhancement, adaptive fractional order differential, Hue, Saturation, Value (HSV) space, foggy image

摘要: 为了提高雾天图像的清晰度,解决分数阶微分阶数取值的单一性问题,提出了一种新的自适应分数阶微分的图像增强方法。基于具有六阶精度的Riesz分数阶微分的近似计算公式,构造了一种新的高精度分数阶微分掩模——RH算子,并对其进行改进,形成了IRH算子。针对图像局部特征建立了分数阶微分函数,提出了一种分数阶微分选取准则,实现了阶数逐点自适应选取的方法。结合IRH算子,形成了自适应IRH图像增强算法。对于彩色图像,由于RGB空间各通道之间独立性低,对各通道增强后再叠加可能会出现颜色失真,因此将图像由RGB空间转化到HSV空间且只对亮度通道进行增强处理。选择一组雾天图像进行了实验,并与Tiansi算子,基于分割的自适应分数阶微分图像增强算法以及自适应分数阶微分的复合双边滤波算法进行了比较,实验结果表明所提算法具有明显的增强效果,并且通过计算信息熵和平均梯度进一步表明了该算法的有效性。

关键词: Riesz分数阶微分, 图像增强, 自适应分数阶微分, HSV空间, 雾天图像

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