Aluminum casting is utilized for making complex objects. Different defects may, however, be introduced during the casting process; the detection of which relies on radiography testing as a standard inspection method. To improve information extraction from the acquired X-ray images, image processing methods are often necessary to improve the contrast of the image features and to increase detection success of hidden defects. In this study, two methods based on the sparse regularization were used to enhance the contrast and defect(s) visualization from the radiographs of different casting objects. The Weighted Encoding with Sparse Nonlocal Regularization (WESNR) and Laplacian Scale Mixture (LSM) with a Nonlocal Low-rank Regularizer (NLR) was used to remove Gaussian and impulse noises from the low contrast images. The proposed algorithms were successfully implemented to radiographic images of the cast objects. The results show that improvements in the visualization of internal struc...

中文翻译：

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通过两种不同的混合噪声消除方法增强铝铸件中的探伤

铝铸件用于制造复杂的物体。但是，在铸造过程中可能会引入不同的缺陷。其检测依赖于射线照相测试作为标准检查方法。为了改善从所获取的X射线图像中提取信息，通常需要图像处理方法来改善图像特征的对比度并增加隐藏缺陷的检测成功率。在这项研究中，使用了基于稀疏正则化的两种方法来增强不同铸造对象的X射线照片的对比度和缺陷可视化。使用稀疏非局部正则化（WESNR）和带非局部低秩正则化器（NLR）的拉普拉斯比例混合（LSM）的加权编码来从低对比度图像中去除高斯和脉冲噪声。所提出的算法已成功应用于铸件的放射线图像。结果表明，改进了内部结构的可视化。