Borehole image adaptive enhancement based on homomorphic filtering and Curvelet transform

HE Fei-jia; LI Qing-wu; HAN Hui; ZHANG Jian-qing; TAN Xian-jiang

doi:10.13873/J.1000-9787(2017)08-0145-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Borehole image adaptive enhancement based on homomorphic filtering and Curvelet transform

更新时间：2025-09-11

- Borehole image adaptive enhancement based on homomorphic filtering and Curvelet transform
- Vol. 36, Issue 8, Pages: 145-148(2017)
- 作者机构：
  
  1. 河海大学物联网工程学院
  2. 常州市传感网与环境感知重点实验室
  3. 长江地球物理探测(武汉)有限公司
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)08-0145-04
  CLC： TP391.41
- Published：2017
- 稿件说明：
移动端阅览
[1]何飞佳,李庆武,韩辉,张建清,谭显江.基于同态滤波与Curvelet变换的钻孔图像自适应增强[J].传感器与微系统,2017,36(08):145-148.

HE Fei-jia, LI Qing-wu, HAN Hui, et al. Borehole image adaptive enhancement based on homomorphic filtering and Curvelet transform[J]. 2017, 36(8): 145-148.
[1]何飞佳,李庆武,韩辉,张建清,谭显江.基于同态滤波与Curvelet变换的钻孔图像自适应增强[J].传感器与微系统,2017,36(08):145-148. DOI： 10.13873/J.1000-9787(2017)08-0145-04.

HE Fei-jia, LI Qing-wu, HAN Hui, et al. Borehole image adaptive enhancement based on homomorphic filtering and Curvelet transform[J]. 2017, 36(8): 145-148. DOI： 10.13873/J.1000-9787(2017)08-0145-04.

摘要

针对岩石数字钻孔图像存在的光照不均、图像中岩石表面边缘细节模糊等情况

提出了一种钻孔图像自适应增强算法。对原图进行同态滤波;使用Curvelet变换分解原图与滤波后的图像

对两者的低频子带使用系数直方图匹配算法

将前者与后者的直方图进行匹配

改善光照不均的影响;对原图的高频子带使用自适应的阈值进行滤波

同时利用自适应增强函数进行增强;使用Curvelet反变换重构得到增强后的图像。实验结果表明:算法可以有效地改善钻孔图像光照不均的问题

增强图像中物体的边缘信息

在主观视觉效果和图像客观评价指标上相对于其他算法均有一定优势。

Abstract

To solve problems such as uneven illumination and fuzzy details of rock surface edge are shown in digital borehole images

an adaptive image enhancement algorithm is proposed. Firstly

homomorphic filtering is used to original image. Then

the original image and the filtered image are decomposed by Curvelet transform while the coefficient histogram matching algorithm is used for the low frequency subband of the two. The histogram of the former is matched with the latter to improve effect of illumination unevenness. Next

the high-frequency subband is filtered by an adaptive threshold and is enhanced with an adaptive enhancement function. Finally

the enhanced image is reconstructed by Curvelet inverse transform. The experimental results show that the illumination unevenness is improved effectively and the edge information is enhanced obviously. Certain advantages compared with other algorithms are shown in both subjective and objective evaluation indices.

关键词

Keywords

references

基于非下采样Contourlet变换系数直方图匹配的自适应图像增强 [J]. 周妍,李庆武,霍冠英. 光学精密工程 . 2014(08)

Curvelet变换域侧扫声纳图像增强算法 [J]. 盛惠兴,孟凡玲,李庆武,马国翠,曹晔锋. 海洋测绘 . 2012(01)

基于非下采样Contourlet变换的SAR图像增强 [J]. 沙宇恒,刘芳,焦李成. 电子与信息学报 . 2009(07)

基于第二代曲波变换算法的检测图像增强 [J]. 陈学堂,张元,杨坤平,陈得民. 传感器与微系统 . 2008(12)

红外图像直方图双向均衡技术研究 [J]. 陈钱,柏连发,张保民. 红外与毫米波学报 . 2003(06)

数字图像处理[M]. 电子工业出版社 , (美) 冈萨雷斯 (Gonzalez, 2007

New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities [J] . Emmanuel J.Candès,David L.Donoho. Comm. Pure Appl. Math. . 2003 (2)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

New algorithm for X-ray image enhancement

New progress of research on image enhancement method

Visual detection algorithm for rail surface defects based on image sensor

Application of improved adaptive immune genetic algorithm in image enhancement

Enhancing algorithm for low-illumination color image based on fuzzy theory

Related Author

ZHAO Cun

HU Wei-wei

LI Yang

CHANG Xia

JI Feng

MAO Zheng-chong

WU Feng

LIU Xiang

Related Institution

Institute of New Electronic Devices and Application,Hangzhou Dianzi University

School of Computer Science and Engineering,Beifang Univesity of Nationality

Institute of Image Processing and Understanding,School of Mathematics and Information Science,Beifang Univesity of Nationality

College of Internet of Things Engineering,Jiangnan University

College of Automation & Electrical Engineering,Lanzhou Jiao Tong University

⁰