摘要
紫外成像仪在低照度环境下检测高压设备外部电晕放电时,输出图像模糊,不利于故障点的定位、分析和识别,有必要采取一定图像处理算法改善图像质量。简要分析了这类紫外图像的特征,提出了滤波和灰度变换的方法,并对图像进行了增强处理。对比分析了目前常用的空域滤波、频域滤波和小波域滤波方法的去噪效果,采用了图像的分段线性灰度变换和自适应直方图均衡算法改善了图像亮度和对比度,工程实际应用验证了所提方法的有效性。
Under the low illumination environment, using the Ultraviolet imager to detect high-voltage equipment external corona discharge, the output image is blurred and affects the location, analysis and recognition of discharge fault, so it is necessary to take certain image processing algorithm to improve the image quality. After a brief analysis of the characteristics of this type of ultraviolet images, the method of image enhancement with filtering and gray level transformation is proposed in this paper. The de-noising effect of spatial filtering, frequency domain filtering and wavelet de-noising is compared and analysis, then by using segmentation gray scale linear transform and adaptive histogram equalization algorithm, the image brightness and contrast is improved. Through practical engineering application, the effectiveness of this method has been verified.
出处
《高压电器》
CAS
CSCD
北大核心
2009年第6期15-19,共5页
High Voltage Apparatus
关键词
电晕
紫外成像
图像去噪
灰度变换
corona
ultraviolet imaging
image de-noising
gray level transformation
