Application of Improved Wavelet Threshold in Infrared Thermal Wave Nondestructive Testing
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摘要: 近二十年来红外热波无损检测技术迅速发展，并在较多领域都得到了普遍应用，但碍于其易受环境影响和工作元件不均匀的特殊性，非制冷红外热像仪原始热波图总存在一定程度的噪声污染，因此对原始热波图进行去噪处理是该技术的关键步骤。传统的改进小波阈值去噪方法局限于对阈值进行自适应分解尺度的改造，使阈值函数平滑连续保真。在噪声方差估计方面没有针对性的方法，而噪声的方差估计是阈值的关键变量，这决定了小波阈值去噪的效果。本文将根据红外图像噪声特性建立混合噪声模型，在噪声模型的基础上进行噪声方差估计、改进阈值及阈值函数，通过软件获取最佳函数参数，最后对仿真模拟结果进行分析，对真实图像进行处理评价，结果表明经改进后的小波阈值去噪方法相对于传统阈值去噪方法和部分滤波去噪方法具有更好的去噪效果。Abstract: Infrared thermal wave non-destructive testing is a new type of technology that has developed rapidly in the past two decades and is widely used in many fields. However, owing to its vulnerability to influence from environmental factors and the particularity of its uneven working components, there is always a certain degree of noise pollution in the original thermal image of uncooled thermal imaging cameras; therefore, denoising the original thermal image is a key step in this technology. The traditional improved wavelet threshold denoising method is limited to the transformation of the adaptive decomposition scale of the threshold, such that the threshold function is smooth and continuous. There is no targeted method for noise variance estimation, which is the key variable of the threshold that determines the effect of wavelet threshold denoising. This study establishes a mixed noise model based on the noise characteristics of infrared images, estimates the noise variance, improves the threshold and threshold function based on the noise model, obtains the best function parameters through software, and finally analyzes the simulation results, process, and evaluation of real images. The results show that the improved wavelet threshold denoising method has a better denoising effect than the traditional threshold denoising method and partial filter denoising method.
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Key words:
- wavelet threshold /
- infrared non-destructive testing /
- simulation /
- noise modeling
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图 5 仿真图像不同去噪方法处理结果
Figure 5. Simulation image processing results of different denoising methods
图 6 真实图像不同去噪方法处理结果
Figure 6. Real image processing results of different denoising methods
表 1 仿真模拟图像几种去噪方法评价指标对比
Table 1. Comparison of evaluation indexes of several image denoising merhods
Denoising method MSE PSNR SSIM Noisy image 0.2866 2.4322 0.5512 Hard threshold function 0.2712 2.5127 0.2896 Soft threshold function 0.2755 2.6893 0.5924 Median filter 0.2781 2.7667 0.4612 Improved threshold function 0.2877 3.5389 0.6415 
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