信赖域方法在红外图像序列处理中的应用

Application of trust region method in infrared image sequence processing

在以光源为激励的红外无损检测图像序列采集过程中,由于受到不均匀加热、环境辐射等因素影响,采集到的图像序列存在着背景噪声大、对比度低、缺陷显示效果差等问题,易造成缺陷的漏检。为提高缺陷检出率,提出了基于信赖域反射算法的红外图像序列处理技术。通过算法对加热不均造成的背景噪声进行快速曲面拟合,并将拟合得到的背景曲面从原始图像中减去,从而去除加热不均的背景噪声。利用主成分分析算法对去除背景后的图像序列进行缺陷特征信息提取,进一步提高红外图像的信噪比。结合区域生长算法对缺陷区域进行分割,以提取缺陷区域。实验结果表明:采用上述方法,能够有效地改善红外图像的信噪比,进而达到提高缺陷检出率的目的。

In the process of collecting thermal images of infrared nondestructive testing(NDT)with light source as the excitation,due to the influence of uneven heating,environmental radiation and other factors,the collected thermal image sequence has problems such as high background noise,low contrast and poor display effect of defects,which are easy to cause the omission of defects.In order to improve the defect detection rate,infrared thermal image sequence processing technology based on Trust Region Reflective(TRR)algorithm was proposed.Firstly,the background noise surface with uneven heating was fitted by TRR algorithm,and the background surface obtained by fitting was subtracted from the original thermal images to remove the background noise caused by uneven heating.Then,Principal Component Analysis(PCA)algorithm was used to extract the defect feature information of the thermal image sequence after removing the background,so as to further improve the signal-to-noise ratio of the infrared thermal wave images.Finally,the defect region was segmented by regiongrowing algorithm.The experimental results show that a combination of these algorithms can effectively improve the signal-to-noise ratio of the infrared thermal image,thus improve the defect detection rate.