系统工程与电子技术
繫統工程與電子技術
계통공정여전자기술
SYSTEMS ENGINEERING AND ELECTRONICS
2015年
2期
443-448
,共6页
无损检测%混沌变异双粒子群优化%灰度级变换%自适应增强%Contourlet 变换
無損檢測%混沌變異雙粒子群優化%灰度級變換%自適應增彊%Contourlet 變換
무손검측%혼돈변이쌍입자군우화%회도급변환%자괄응증강%Contourlet 변환
nondestructive testing%chaotic variation double particle swarms optimization%gray-scale trans-form%adaptive enhancement%contourlet transform
针对无损检测红外热波图像对比度低、边缘模糊、含大量噪声的问题，提出了基于 Contourlet 变换和混沌变异双粒子群优化（adaptive chaotic variation particle swarm optimization，ACPSO）的自适应增强方法。红外热波图像经 Contourlet 变换分解成低通和带通方向子带。低通子带系数依据一种适应于人类视觉系统的灰度级变换调整，待定参数由 ACPSO 确定，为了得到最佳增强效果，适应度函数由一种对比度测量函数确定；带通方向子带系数的调整则采用非线性增益函数实现，从而抑制噪声并增强细节。大量红外热波图像增强实验结果表明，与现有的4种增强方法相比，能大大提高缺陷和背景之间的对比度，增强缺陷的边缘细节。进一步采用倒数熵多阈值分割方法时，能更有效地提取缺陷，为后续准确进行缺陷识别和尺寸测量奠定了基础。
針對無損檢測紅外熱波圖像對比度低、邊緣模糊、含大量譟聲的問題，提齣瞭基于 Contourlet 變換和混沌變異雙粒子群優化（adaptive chaotic variation particle swarm optimization，ACPSO）的自適應增彊方法。紅外熱波圖像經 Contourlet 變換分解成低通和帶通方嚮子帶。低通子帶繫數依據一種適應于人類視覺繫統的灰度級變換調整，待定參數由 ACPSO 確定，為瞭得到最佳增彊效果，適應度函數由一種對比度測量函數確定；帶通方嚮子帶繫數的調整則採用非線性增益函數實現，從而抑製譟聲併增彊細節。大量紅外熱波圖像增彊實驗結果錶明，與現有的4種增彊方法相比，能大大提高缺陷和揹景之間的對比度，增彊缺陷的邊緣細節。進一步採用倒數熵多閾值分割方法時，能更有效地提取缺陷，為後續準確進行缺陷識彆和呎吋測量奠定瞭基礎。
침대무손검측홍외열파도상대비도저、변연모호、함대량조성적문제，제출료기우 Contourlet 변환화혼돈변이쌍입자군우화（adaptive chaotic variation particle swarm optimization，ACPSO）적자괄응증강방법。홍외열파도상경 Contourlet 변환분해성저통화대통방향자대。저통자대계수의거일충괄응우인류시각계통적회도급변환조정，대정삼수유 ACPSO 학정，위료득도최가증강효과，괄응도함수유일충대비도측량함수학정；대통방향자대계수적조정칙채용비선성증익함수실현，종이억제조성병증강세절。대량홍외열파도상증강실험결과표명，여현유적4충증강방법상비，능대대제고결함화배경지간적대비도，증강결함적변연세절。진일보채용도수적다역치분할방법시，능경유효지제취결함，위후속준학진행결함식별화척촌측량전정료기출。
The infrared thermal wave image in the nondestructive testing have the disadvantages of low con-trast,blurred edges and strong noise.Thus an adaptive enhancement method of the infrared thermal wave im-age based on contourlet transform and adaptive chaotic variation particle swarm optimization (ACPSO)is pro-posed.An infrared thermal wave image is decomposed into a low-pass subband and band-pass directional subba-nds through the contourlet transform.Then the coefficients of low-pass subband are adjusted according to a gray-scale transform,which is adapted to the human visual system.The related parameters are determined by ACPSO.In order to obtain the best enhancement effect,the fitness function can measure the contrast of ima-ges.While the coefficients of band-pass directional subbands are adjusted by a nonlinear gain function.Thus noise is suppressed and details are enhanced.A large number of experimental results of infrared thermal wave image enhancement show that,compared with four existing image enhancement methods,the proposed method can improve the contrast between the defects and the background greatly,enhance defect edges and suppress noise.While multi-thresholding method using maximum reciprocal entropy is further adopted,the defects are exacted more efficiently.The proposed method lays the foundation for the subsequent accurate defect recogni-tion and measurement of defect sizes.
