Fahad Muhammad, Zhang Tao, Khan Sajid Ullah, Albanyan Abdullah, Siddiqui Fazeela, Iqbal Yasir, Zhao Xin, Geng Yanzhang
School of Electrical and Information Engineering, Tianjin University, Nankai District, Tianjin, China.
College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, KSA.
J Xray Sci Technol. 2024;32(6):1553-1570. doi: 10.3233/XST-240227.
Airport security is still a main concern for assuring passenger safety and stopping illegal activity. Dual-energy X-ray Imaging (DEXI) is one of the most important technologies for detecting hidden items in passenger luggage. However, noise in DEXI images, arising from various sources such as electronic interference and fluctuations in X-ray intensity, can compromise the effectiveness of object identification.
To address the challenge of noise interference in DEXI, this study aims to develop and validate a robust denoising technique using the Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT).
The proposed method targets and removes background and Poisson noise in DEXI images, improving object recognition accuracy. During the denoising process, images are decomposed into several subbands, and thresholding techniques are applied to minimize noise while preserving important information. The images are then reconstructed to provide a cleaner and more accurate depiction of scanned objects.
Experimental results demonstrate the effectiveness of the DWT and SWT-based denoising strategy in preserving critical data while suppressing noise in DEXI. The performance of the denoising technique is quantified using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). The proposed system achieved an average PSNR of 35.23 and an MSE of 19.52 for 256×256 DEXI images, and an average PSNR of 36.01 and an MSE of 16.29 for 512×512 DEXI images.
The results highlight the achievement of the proposed approach in enhancing the quality of DEXI for improved security screening, demonstrating its potential application in airport security systems.
机场安检仍是确保乘客安全和阻止非法活动的主要关注点。双能X射线成像（DEXI）是检测乘客行李中隐藏物品的最重要技术之一。然而，DEXI图像中的噪声，源于电子干扰和X射线强度波动等各种来源，可能会影响物体识别的有效性。
为应对DEXI中噪声干扰的挑战，本研究旨在开发并验证一种使用离散小波变换（DWT）和平移不变小波变换（SWT）的强大去噪技术。
所提出的方法针对并去除DEXI图像中的背景和泊松噪声，提高物体识别准确率。在去噪过程中，图像被分解为几个子带，并应用阈值技术在保留重要信息的同时最小化噪声。然后对图像进行重建，以提供对扫描物体更清晰、更准确的描绘。
实验结果证明了基于DWT和SWT的去噪策略在保留关键数据同时抑制DEXI噪声方面的有效性。使用峰值信噪比（PSNR）和均方误差（MSE）对去噪技术的性能进行量化。对于256×256的DEXI图像，所提出的系统平均PSNR为35.23，MSE为19.52；对于512×512的DEXI图像，平均PSNR为36.01，MSE为16.29。
结果突出了所提出方法在提高DEXI质量以改进安全筛查方面的成果，证明了其在机场安检系统中的潜在应用。
