铁道学报
鐵道學報
철도학보
2015年
2期
58-63
,共6页
裂纹红外图像%自适应增强%非子采样Contourlet变换%多尺度积阈值
裂紋紅外圖像%自適應增彊%非子採樣Contourlet變換%多呎度積閾值
렬문홍외도상%자괄응증강%비자채양Contourlet변환%다척도적역치
infrared image of a crack%adaptive enhancement%Nonsubsampled Contourlet Transform(NSCT)%Adaptive Multiscale Products Thresholding(AMPT)
利用红外热波技术检测钢轨斜裂纹伤损所得的红外图像存在整体亮度偏暗、边缘对比度低和纹理细节模糊的缺点,对测量裂纹的扩展深度不利。对此,本文提出一种基于非子采样Contourlet变换 NSCT (Nonsub‐sampled Contourlet Transform)的自适应多尺度积阈值AMPT(Adaptive Multiscale Products Thresholding)钢轨裂纹红外图像增强算法。对图像进行NSCT分解得到不同尺度不同方向上的子带系数,由子带系数自适应地确定阈值并调整增强函数,将阈值作用于每一子带的多尺度积,对阈值化操作后的子带系数进行增强处理和逆变换,实现图像增强。实验结果表明:该算法在抑制噪声和提高图像整体对比度的基础上,有效突出了目标图像的边缘,对裂纹内部微弱细节纹理的增强效果优于其他传统的红外图像增强方法,为进一步提取代表裂纹深度的关键像素提供了有力支撑。
利用紅外熱波技術檢測鋼軌斜裂紋傷損所得的紅外圖像存在整體亮度偏暗、邊緣對比度低和紋理細節模糊的缺點,對測量裂紋的擴展深度不利。對此,本文提齣一種基于非子採樣Contourlet變換 NSCT (Nonsub‐sampled Contourlet Transform)的自適應多呎度積閾值AMPT(Adaptive Multiscale Products Thresholding)鋼軌裂紋紅外圖像增彊算法。對圖像進行NSCT分解得到不同呎度不同方嚮上的子帶繫數,由子帶繫數自適應地確定閾值併調整增彊函數,將閾值作用于每一子帶的多呎度積,對閾值化操作後的子帶繫數進行增彊處理和逆變換,實現圖像增彊。實驗結果錶明:該算法在抑製譟聲和提高圖像整體對比度的基礎上,有效突齣瞭目標圖像的邊緣,對裂紋內部微弱細節紋理的增彊效果優于其他傳統的紅外圖像增彊方法,為進一步提取代錶裂紋深度的關鍵像素提供瞭有力支撐。
이용홍외열파기술검측강궤사렬문상손소득적홍외도상존재정체량도편암、변연대비도저화문리세절모호적결점,대측량렬문적확전심도불리。대차,본문제출일충기우비자채양Contourlet변환 NSCT (Nonsub‐sampled Contourlet Transform)적자괄응다척도적역치AMPT(Adaptive Multiscale Products Thresholding)강궤렬문홍외도상증강산법。대도상진행NSCT분해득도불동척도불동방향상적자대계수,유자대계수자괄응지학정역치병조정증강함수,장역치작용우매일자대적다척도적,대역치화조작후적자대계수진행증강처리화역변환,실현도상증강。실험결과표명:해산법재억제조성화제고도상정체대비도적기출상,유효돌출료목표도상적변연,대렬문내부미약세절문리적증강효과우우기타전통적홍외도상증강방법,위진일보제취대표렬문심도적관건상소제공료유력지탱。
The infrared image obtained from the use of infrared thermal wave technology to detect an oblique crack of a rail has a number of shortcomings , including a low overall brightness , low edge contrast and blurred minutiae texture ,which would result in detrimental impact on the measurement of the depth of the crack ex‐pansion . Therefore in this paper , a nonsubsampled Contourlet transform (NSCT ) based adaptive multiscale productsthresholding(AMPT)algorithmisproposedtoenhancetheinfraredimageofarailcrack.Firstly,the image was decomposed using the NSCT to obtain multi‐scale and multi‐direction sub‐band coefficients , which then adaptively selected the threshold values and adjusted the enhancement function;subsequently the thresh‐old w as applied to the multi‐scale product of each sub‐band;and finally the thresholding processed sub‐band coefficients were enhanced and the results underwent a further inverse transformation , thereby realizing the image enhancement . The results of the experiment demonstrate that the proposed algorithm can suppress the noise ,increase the overall image contrast , while effectively highlight the edge of the target image . This ap‐proach is superior in inner‐crack minutiae texture enhancement to traditional infrared image enhancing meth‐ods , therefore providing a powerful support for the further extraction of key pixels that can characterize the depth of a crack .