基于改进小波阈值的红外热波无损检测图像噪声抑制方法的研究
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摘要
针对红外热波无损检测技术在应用过程中红外图像信噪比较低、细节不明了的问题,分析了小波阈值降噪方法的理论原理和实现步骤,针对传统的小波硬阈值、软阈值函数在去噪中的不足之处,提出了改进的阈值函数。改进的阈值函数通过引入调节因子,改善了硬阈值去噪在小波系数重构时可能会产生振荡的现象,软阈值去噪函数存在固定差异问题,且具有一定的灵活性和自适应性。实验结果表明:采用改进阈值去噪方法在有效的减少图像噪声的同时,能保留更多图像细节信息,去噪后的红外图像具有更高的信噪比。
In view of the low signal-to-noise ratio of infrared image in the application process, the theoretical principle and implementation steps of wavelet threshold de-noising method are analyzed. The traditional wavelet hard threshold and soft threshold are analyzed. An improved threshold function is proposed for the inadequacy of functions in de-noising. The improved threshold function improves the phenomenon that the hard threshold de-noising may produce oscillation when the wavelet coefficients are reconstructed by introducing the adjustment factor, and the soft threshold de-noising function has a fixed difference problem, and has certain flexibility and adaptability. The experimental results show that the improved threshold de-noising method can effectively reduce image noise while retaining more image detail information and the de-noised infrared image has higher signal-to-noise ratio.
In view of the low signal-to-noise ratio of infrared image in the application process, the theoretical principle and implementation steps of wavelet threshold de-noising method are analyzed. The traditional wavelet hard threshold and soft threshold are analyzed. An improved threshold function is proposed for the inadequacy of functions in de-noising. The improved threshold function improves the phenomenon that the hard threshold de-noising may produce oscillation when the wavelet coefficients are reconstructed by introducing the adjustment factor, and the soft threshold de-noising function has a fixed difference problem, and has certain flexibility and adaptability. The experimental results show that the improved threshold de-noising method can effectively reduce image noise while retaining more image detail information and the de-noised infrared image has higher signal-to-noise ratio.
引文
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[2]江海军,陈力.闪光灯热激励红外热波成像无损检测设备及应用[J].无损检测,2017.9(12):20-22
[3]赵钦佩.复杂背景条件下的红外图像预处理检测方法研究[D]上海交通大学,2007:103-13
[4]常本康,蔡毅编.红外成像阵列与系统[M].科学出版社,2006.
[5]Donoho D L.De-noising by Soft-thresholding[J].IEEETransaction on Information Theory,1995.41(3):613-627
[6]Khare A,Khare M,Jeong Y.Despeckling of medica ultrasound images using Daubechies complex wavele transform[J].Signal Process,2010.90(2):428-439
[7]HIEN M,etal.Denoising MR spectroscopic imaging data with low-rank approximations[J].IEEE Transactions on Biomedical Engineering,2013.60(1):78-89
[8]周西峰,朱文文,郭前岗.基于渐近半软阈值函数的超声信号去噪方法[J].探测与控制学报,2011.33(2):35-39
[9]张小燕,吐尔洪江·阿布都克力木.小波变换的阈值图像去噪算法改进[J].计算机技术与发展,2017.3:81-84
[10]杨立.基于改进小波阈值函数的图像去噪[J].重庆理工大学学报:自然科学,2013.27(2):93-95
[11]施兢业,刘俊,陆龚琪.电力设备红外图像的小波阈值去噪方法研究[J].光学技术,2017.43(2):162-165
[12]沙俊名,刘泽乾,庞帅,夏志申.改进的小波阈值算法在红外图像去噪中的应用[J].弹箭与制导学报,2012.32(3):35-38