Preprocessing for highly reflective surface defect image

YANG Yong-min; FAN Ji-zhuang; ZHAO Jie

doi:10.3788/OPE.20101810.2288

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Preprocessing for highly reflective surface defect image

Article | 更新时间：2020-08-12

- Preprocessing for highly reflective surface defect image
- Optics and Precision Engineering Vol. 18, Issue 10, Pages: 2288-2296(2010)
- 作者机构：
  
  哈尔滨工业大学机器人技术与系统国家重点实验室,黑龙江哈尔滨,150080
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20101810.2288
  CLC： TP391.4
- Received：11 December 2009，
  
  Revised：10 March 2010，
  
  Published Online：28 October 2010，
  
  Published：20 October 2010
- 稿件说明：
移动端阅览
杨永敏, 樊继壮, 赵杰. 强反射表面缺陷图像预处理[J]. 光学精密工程, 2010,18(10): 2288-2296

YANG Yong-min, FAN Ji-zhuang, ZHAO Jie. Preprocessing for highly reflective surface defect image[J]. Editorial Office of Optics and Precision Engineering , 2010,18(10): 2288-2296
杨永敏, 樊继壮, 赵杰. 强反射表面缺陷图像预处理[J]. 光学精密工程, 2010,18(10): 2288-2296 DOI： 10.3788/OPE.20101810.2288.

YANG Yong-min, FAN Ji-zhuang, ZHAO Jie. Preprocessing for highly reflective surface defect image[J]. Editorial Office of Optics and Precision Engineering , 2010,18(10): 2288-2296 DOI： 10.3788/OPE.20101810.2288.

摘要

为了校正强反射表面图像的亮度不均匀性

抑制局部强亮度现象

同时使其保留足够多的原始图像信息

研究了强反射表面缺陷图像的预处理方法。分析了强反射表面的光学特性

研究了偏微分方程理论及同态滤波算法

在此基础上提出了一种基于偏微分方程的同态滤波算法

即采用热传导方程滤波算子对同态分解的图像进行处理。引入信息熵对不同尺度参数下的处理效果进行评价

从而确定热传导同态滤波的尺度参数

并将该算法与背景去除法、基于小波的同态滤波算法进行了比较。对比实验表明

采用提出的算法处理后得到的图像其灰度级均匀分布在1~6之间

信息熵值均在91%以上。该方法校正了图像亮度的不均匀性

提高了图像质量

同时能保留足够多的原始图像信息。

Abstract

In order to modify the luminance heterogeneity of a highly reflective surface image

restrain the phenomenon of local high luminance and to preserve enough original image information

a preprocessing method for highly reflective surface defect image is investigated and the optical characteristics of a highly reflective surface are analyzed by using Partial Differential Equation(PDE) theory and homomorphic filtering algorithms. A homomorphic filtering algorithm based on the PDE is proposed which uses the heat conduction equation filtering operator to process homomorphic decomposition images. The comentropy is induced to evaluate the processing effect in different dimension parameters

then the dimension parameter of heat conduction homomorphic filtering is determined. The algorithm is contrasted with the background subtraction algorithm and wavelet homostasis filtering algorithm

and the experiment indicates that the image processed by the proposed algorithm has the gray-level values distributed uniformly between 1~6 and a high comentropy value above 91%. This method modifies the image luminance heterogeneity

improves image quality and preserves sufficient original image information.

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references

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