ABSTRACT The life of an aircraft depends on the early detection and removal of corrosion in its structure. The importance of detecting corrosion cannot be understated, because corrosion can cause other kinds of damage, such as cracks. Radiography is an important method for the detection of hidden defects in aircraft structure. To maximize information extraction from the radiographic images, the noise of the system should be minimized, or the contrast of the defective region should be maximized by different methods. The development of effective image processing methods, within both the spatial and frequency domains, is important to the research of industrial radiographic testing. In this study, the geometric locally adaptive sharpening method was used to improve hidden structure visualization of details and defects from aircraft part radiographs. The method relies on sharpening by using the steering kernel regression method. Here, the enhancing contrast and sharpening algorithm are effectively mixed together. The proposed algorithm was successfully applied to radiographic images of aircraft parts. An improvement of the structure detail visualization and defect region detection was achieved by sharpening the edges and preserving fine detail imaging information. Experts’ reviews showed that defect regions from the geometric locally adaptive sharpening reconstructed images were better visualized than the original images. Also, the resulting evaluation of the output images shows that the edges are sharpened by the proposed method and that the background of the image decreases to zero.

中文翻译：

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基于几何局部自适应锐化的飞机结构材料数字射线图像缺陷检测

摘要 飞机的寿命取决于及早发现和清除其结构中的腐蚀。不能低估检测腐蚀的重要性，因为腐蚀会导致其他类型的损坏，例如裂纹。射线照相是检测飞机结构隐藏缺陷的重要方法。为了最大限度地从放射图像中提取信息，应尽量减少系统的噪声，或者应通过不同的方法最大限度地提高缺陷区域的对比度。在空间和频率域内开发有效的图像处理方法对于工业射线照相测试的研究非常重要。在这项研究中，几何局部自适应锐化方法用于改善飞机零件射线照片中细节和缺陷的隐藏结构可视化。该方法依赖于使用转向核回归方法进行锐化。在这里，增强对比度和锐化算法有效地混合在一起。所提出的算法成功地应用于飞机部件的射线照相图像。通过锐化边缘和保留精细细节成像信息，实现了结构细节可视化和缺陷区域检测的改进。专家评审表明，几何局部自适应锐化重建图像中的缺陷区域比原始图像更好地可视化。此外，输出图像的结果评估表明，所提出的方法使边缘锐化，并且图像的背景减少到零。增强对比度和锐化算法有效地混合在一起。所提出的算法已成功应用于飞机零件的射线照相图像。通过锐化边缘和保留精细细节成像信息，实现了结构细节可视化和缺陷区域检测的改进。专家评审表明，几何局部自适应锐化重建图像中的缺陷区域比原始图像更好地可视化。此外，输出图像的结果评估表明，所提出的方法使边缘锐化，并且图像的背景减少到零。增强对比度和锐化算法有效地混合在一起。所提出的算法已成功应用于飞机零件的射线照相图像。通过锐化边缘和保留精细细节成像信息，实现了结构细节可视化和缺陷区域检测的改进。专家评审表明，几何局部自适应锐化重建图像中的缺陷区域比原始图像更好地可视化。此外，输出图像的结果评估表明，所提出的方法使边缘锐化，并且图像的背景减少到零。通过锐化边缘和保留精细细节成像信息，实现了结构细节可视化和缺陷区域检测的改进。专家评审表明，几何局部自适应锐化重建图像中的缺陷区域比原始图像更好地可视化。此外，输出图像的结果评估表明，所提出的方法使边缘锐化，并且图像的背景减少到零。通过锐化边缘和保留精细细节成像信息，实现了结构细节可视化和缺陷区域检测的改进。专家评审表明，几何局部自适应锐化重建图像中的缺陷区域比原始图像更好地可视化。此外，输出图像的结果评估表明，所提出的方法使边缘锐化，并且图像的背景减少到零。