基于三级图像融合与CLAHE的机场安检X光图像增强算法

针对CLAHE增强机场安检X光图像出现的颜色失真及背景噪声放大问题,提出一种基于三级图像融合与CLAHE的X光图像增强算法.首先将X光图像分别转换成RGB、HSV图像并分别进行CLAHE增强,将增强后的图像通过欧几里得范数实现第1级融合.然后将融合后的图像进行USM锐化,锐化过程中根据掩膜进行图像第2级融合.最后将锐化后的图像与原图像按系数进行合并,完成第3级融合.仿真实验结果表明,本文提出的算法能够有效提高安检X光图像的对比度, PSNR平均值提高了7 dB,同时可以抑制增强图像颜色失真及背景噪声.该算法有助于提高X光图像中违禁物品的识别准确率,对平安机场的构建具有积极意义.

Airport Security X-ray Image Enhancement Algorithm Based on Three-level Image Fusion and CLAHE

Given the problems of color distortion and background noise amplification using contrast limited adaptive histogram equalization (CLAHE) to enhance X-ray images of airport security inspection, this study proposes an X-ray image enhancement algorithm based on three-level image fusion and CLAHE. Specifically, the X-ray image is converted into RGB and HSV images for CLAHE enhancement respectively. The enhanced images are fused by the Euclidean norm for the first-level fusion. Then, the fused images are sharpened by unsharp masking (USM), during which the second-level image fusion is performed according to the mask. Finally, the sharpened images and the original image are combined according to the coefficient to complete the third-level fusion. The simulation experiment results show that the proposed algorithm effectively improves the contrast of security inspection X-ray images, increases the average peak signal-to-noise ratio (PSNR) by 7 dB, and suppresses the color distortion and background noise in the enhanced images. This algorithm helps to improve the accuracy of identifying prohibited items in X-ray images and thus has a positive impact on the construction of a safe airport.