赵谦;钱渠;任志奇;

• 1:西安科技大学通信与信息工程学院

摘要(Abstract)：

应用一种基于二维经验模态分解和高频滤波的图像增强算法,对处于恶劣环境中矿井下的图像进行增强处理,能有效解决此类图像存在边缘及纹理等局部细节信息模糊、对比度不高以及对噪声敏感等问题。首先,对输入的矿井下图像进行高通滤波处理,去除图像中的高频成分,得到矿井下图像的低频部分;其次,用二维经验模态分解出图像的高频部分,弥补因第1步引起的图像细节信息丢失的不足;再次,通过确定高低频比例因子c,将提取的高频细节与低频背景按3∶2的比例融合,并有效抑制粉尘散射模糊和过曝光白色伪影现象的噪声;最后,采用直方图均衡化来平衡图像灰度,增强图像的细节,提高图像整体的对比度。对比实验表明,在保证图像质量的前提下,所提算法与传统的高频强调滤波相比,处理后的图像清晰度Brenner指标提高10%,均方误差更小,在增强矿井下图像边缘纹理以及暗部细节效果明显,能够有效提高图像的对比度,增强图像的亮度和信息熵,能对后续的图像处理及分析提供有效的帮助。

关键词(KeyWords)： 图像增强;矿井图像;高通滤波;二维经验模态分解

Abstract:

Keywords:

基金项目(Foundation): 陕西省科技计划工业科技攻关(2015GY023,2017GY-073);; 西安市碑林区应用技术研发(GX1811)

作者(Author): 赵谦;钱渠;任志奇;

Email:

DOI: 10.13800/j.cnki.xakjdxxb.2020.0315

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