结合NSST显著性检测及图割的泡沫红外图像分割

陈诗媛; 廖一鹏; 张进; 王卫星

doi:10.37188/CJLCD.2020-0234

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结合NSST显著性检测及图割的泡沫红外图像分割

图像处理 | 更新时间：2021-04-02

- 结合NSST显著性检测及图割的泡沫红外图像分割
- Foam infrared image segmentation combining NSST saliency detection and graph cuts
- 液晶与显示 2021年36卷第4期页码：584-595
- 作者机构：
  
  福州大学物理与信息工程学院, 福建福州 350108
- 作者简介：
  
  [ "陈诗媛(1995-), 女, 广西柳州人, 研究生, 2018年于福州大学获得学士学位, 主要从事机器视觉方面的研究。E-mail:250407773@qq.com" ]
  廖一鹏, E-mail：wxwwx@fzu.edu.cn
  [ "王卫星(1959-), 男, 湖南邵阳人, 博士, 教授, 1997年于瑞典皇家工学院获得邵阳博士学位, 主要从事图像处理与模式识别、机器视觉应用等方面研究。E-mail: wxwwx@fzu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61471124);国家自然科学基金(61601126);福建省自然科学基金(2019J01224)
- DOI：10.37188/CJLCD.2020-0234
  中图分类号： TP391
- 收稿日期：2020-09-09，
  
  修回日期：2020-10-22，
  
  纸质出版日期：2021-04
- 稿件说明：
移动端阅览
陈诗媛, 廖一鹏, 张进, 等. 结合NSST显著性检测及图割的泡沫红外图像分割[J]. 液晶与显示, 2021,36(4):584-595.

Shi-yuan CHEN, Yi-peng LIAO, Jin ZHANG, et al. Foam infrared image segmentation combining NSST saliency detection and graph cuts[J]. Chinese journal of liquid crystals and displays, 2021, 36(4): 584-595.
陈诗媛, 廖一鹏, 张进, 等. 结合NSST显著性检测及图割的泡沫红外图像分割[J]. 液晶与显示, 2021,36(4):584-595. DOI： 10.37188/CJLCD.2020-0234.

Shi-yuan CHEN, Yi-peng LIAO, Jin ZHANG, et al. Foam infrared image segmentation combining NSST saliency detection and graph cuts[J]. Chinese journal of liquid crystals and displays, 2021, 36(4): 584-595. DOI： 10.37188/CJLCD.2020-0234.

摘要

为精确提取浮选泡沫表面的崩塌、新合成气泡，减少噪声和光照影响，提出一种结合非下采样Shearlet变换（NSST）域显著性检测及图割的泡沫红外图像分割方法。对泡沫红外图像进行NSST多尺度分解，采用GBVS算法对低频子带图像进行显著性检测，通过马尔科夫链特征差异计算显著性值，对各个高频方向子带进行噪声系数去除和边缘、弱边缘系数非线性增强。对处理后的多尺度高频子带、低频子带图像进行NSST重构，通过低频子带图像的显著性检测结果构建显著性约束项，采用高斯拟合函数构建气泡亮度约束项，然后构造图割能量函数，最后利用最大流/最小割算法对目标区域进行分割。实验结果表明：该方法受光照影响小，一定程度上解决了过分割和欠分割问题。正常浮选检测准确率为91.8%，欠浮选为87.1%，过浮选为88.9%，分割精度较现有方法有明显提高，能有效提取出崩塌或新合成的气泡，表现出良好的抗噪性，且在不同工况下均表现出良好的鲁棒性。

Abstract

In order to accurately extract the collapse of the surface of the flotation foam

newly synthesized bubbles

and reduce the impact of noise and light

a foam infrared image segmentation method combining non-downsampling Shearlet Transform (NSST) domain saliency detection and graph cutting is proposed. NSST multi-scale decomposition of the foam infrared image

saliency detection of low-frequency subband images using GBVS algorithm

saliency value calculation based on Markov chain feature differences

noise coefficient removal and edge

weak edge coefficients are non-linearly enhanced. NSST reconstruction of the processed multi-scale high-frequency subband and low-frequency subband images is performed. The significance constraint term is constructed based on the saliency detection results of the low-frequency subband image

and the bubble brightness constraint term is constructed using a Gaussian fitting function. Then

the map is constructed Graph (Cut Cuts) energy function

and finally the maximum flow/minimum cut algorithm is used to segment the target area. The experimental results show that this method is less affected by light

and to some extent

it solves the problems of over-segmentation and under-segmentation. The detection accuracy rate of normal flotation under-flotation

and over-flotation are 91.8%

87.1%

88.9%

respectively. The segmentation accuracy is significantly improved compared with the existing methods. It can effectively extract collapsed or newly synthesized bubbles

and shows good noise immunity. It has good robustness under different working conditions.

关键词

Keywords

references

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