民航行李X光安检图像自适应小波去噪算法

对X光安检图像进行去噪处理可以更清楚地显示出危险物。针对传统小波软硬阈值去噪方法的缺陷．给出了一种自适应阈值的去噪方法，在此方法中，首先进行软硬阈值的折中处理，然后分别对大于和小于阈值的两部分进行自适应处理。利用Tennessee大学提供的实际采集的X光安栓图像进行实验，结果表明该方法能有效地去除安检图像中的噪声，图像去噪后的信噪比、峰值信噪比分别是25．2229dB、27．708dB．与含噪图像相比分别增加了7．7253dB和7．739dB。因此该方法表现出较高的去噪性能。

Adaptive Wavelet Denoising Algorithm on Civil Aviation Security Inspection Images

The denoising processing for X-ray images will help to show dangerous goods more explicitly. A wavelet denoising method based on the improved adaptive threshold was proposed to conquer the limitations of traditional wavelet denoising methods. In the method, the median is obtained for the soft and hard thresholds for the first step. Afterwards, the adaption is performed for values above or below the threshold individually. The proposed method was applied for security-inspection image processing. In particular, the method is used to process the X-ray security-inspection images offered by the University of Tennessee. The obtained SNR and PSNR are 25.222 9 dB and 27.708 dB, which are 7.725 3 dB and 7.739 dB larger than for the original images. The experimental resuhs have shown that the method is sufficient to suppress the noise in the images.