Image edge detection based on guided filter Retinex and adaptive Canny

WANG Dong-yun; TANG Chu; E Shi-ju; GAO Chun-fu; GE Bing-zao

doi:10.37188/OPE.20212902.0443

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Image edge detection based on guided filter Retinex and adaptive Canny

Information Sciences | 更新时间：2026-09-12

- Image edge detection based on guided filter Retinex and adaptive Canny
- Optics and Precision Engineering Vol. 29, Issue 2, Pages: 443-451(2021)
- 作者机构：
  
  1.浙江师范大学浙江省城市轨道交通智能运维技术与装备重点实验室，浙江金华 321004
  2.浙江今飞凯达轮毂股份有限公司，浙江金华 321005
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20212902.0443
  CLC： TP391
- Received：28 October 2020，
  
  Revised：2020-12-02，
  
  Published：15 February 2021
- 稿件说明：
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王冬云,唐楚,鄂世举等.基于导向滤波Retinex和自适应Canny的图像边缘检测[J].光学精密工程,2021,29(02):443-451.

WANG Dong-yun,TANG Chu,E Shi-ju,et al.Image edge detection based on guided filter Retinex and adaptive Canny[J].Optics and Precision Engineering,2021,29(02):443-451.
王冬云,唐楚,鄂世举等.基于导向滤波Retinex和自适应Canny的图像边缘检测[J].光学精密工程,2021,29(02):443-451. DOI： 10.37188/OPE.20212902.0443.

WANG Dong-yun,TANG Chu,E Shi-ju,et al.Image edge detection based on guided filter Retinex and adaptive Canny[J].Optics and Precision Engineering,2021,29(02):443-451. DOI： 10.37188/OPE.20212902.0443.

摘要

机器视觉识别常用金属制品边缘时，表面亮度不均易导致边缘误识别，且传统的边缘检测算法去噪的同时也抑制了大量边缘信息，降低了边缘检测质量。本文提出一种基于导向滤波Retinex和自适应Canny的图像边缘检测算法。该算法采用基于导向滤波的Retinex法得到金属制品图像的反射分量，通过加权分布的自适应伽马校正提升反射分量图像对比度；然后，采用自适应各向异性扩散滤波对增强后图像进行去噪处理，抑制增强后图像的噪声及低对比度纹理，再采用改进四方向Sobel梯度模板提取图像边缘；最后沿用传统Canny算法的非极大值抑制及双阈值分割进一步细化边缘。实验结果表明，新算法检测典型金属小零件时，图像锐度指标由原图的47.11提升至68.39，金属表面的亮度标准差从原图的44.76下降至20.16；噪声指标从原图的1.1下降到0.15左右，并且在去噪的同时较好地保留了图像边缘锐度。新方法有效改善了金属表面图像因亮度不均导致的边缘误识别问题，并且提取的边缘连接性较好。

Abstract

When machine vision recognizes the edges of metal products， uneven surface brightness can easily cause edges to be misidentified. Traditional edge detection algorithms denoise while also suppressing a considerable amount of edge information， which reduces the quality of edge detection. This study proposes an edge detection method that combines guided filtering-based Retinex and adaptive Canny for metal images. The guided filtering-based Retinex method is first used to obtain the reflection component of a metal image. Next， the image contrast of the reflection component is improved using adaptive gamma correction with weighted distribution， and an adaptive anisotropic diffusion filter is employed to denoise the enhanced image and suppress the noise and low-contrast texture. The improved four-direction Sobel gradient template is then adopted to extract the edges of the image. Finally， the non-maximum suppression and dual-threshold segmentation methods of the traditional Canny algorithm are applied to further refine the edges. The test results showed that when the proposed algorithm was used to detect typical metal parts， the image sharpness index increased from 47.11 in the original image to 68.39， and the brightness standard deviation of the metal surface decreased from 44.76 to 20.16. In addition， the noise assessment index dropped from 1.1 in the original images to approximately 0.15， and the sharpness of the image edges was well preserved. The new method effectively solves the edge misrecognition problem caused by uneven brightness in metal surface images， with the extracted edges exhibiting better connectivity.

关键词

Keywords

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