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基于梯度场和非局部均值的复杂工件图像增强算法

Image Enhancement of Complex Workpiece Based on Gradient Field and Non⁃Local Mean
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摘要 由于高动态X射线成像系统与工件自身结构复杂性,复杂异形工件的射线图像往往呈现对比度低或特征信息不明确的问题。针对该问题,本文提出基于梯度场和非局部均值降噪的图像增强算法,提高视觉质量。首先,基于梯度场和局部方差构建对比度的自适应增强模型,提高射线图像的对比度;然后,利用非局部均值对图像进行去噪,特别地,本文构建了基于泊松分布的非局部均值算法进行图像去噪,从而得到增强的对比度场;最后,建立能量泛函并用梯度下降法求解来获得具有微小细节的更高质量的图像。实验部分对3个复杂异形工件的X射线图像进行图像增强和缺陷检测实验,结果证实了本文算法的有效性。 Due to the complexity of a highly dynamic X-ray imaging system and the complex structure of the workpiece itself,X-ray images often exhibit low contrast or unclear feature information.In this work,an image enhancement algorithm was proposed based on gradient field and non-local means to improve visual quality.A contrast adaptive enhancement model was constructed based on gradient field and local variance to improve the contrast of X-ray images;Then,the image was denoised using non-local means.Specifically,this paper constructed a non-local means algorithm based on Poisson distribution for image Poisson denoising,thereby obtaining an enhanced contrast field;Finally,an energy function was estab⁃lished and solved by using the gradient descent method to obtain higher quality images with small details.Experiments with two typical complex workpieces were performed,and the results verified the effective⁃ness of the proposed approach for image enhancement and defect detection.
作者 韩美蓉 陈平 潘晋孝 李坤 陈洪 HAN Meirong;CHEN Ping;PAN Jinxiao;LI Kun;CHEN Hong(School of Mathematics,North University of China,Taiyuan 030051,China;Shanxi Key Lab of Information Detection and Processing,North University of China,Taiyuan 030051,China;The 2nd Research Institute of China Electronics Technology Group Corporation,Taiyuan 030024,China)
出处 《测试技术学报》 2024年第4期401-406,共6页 Journal of Test and Measurement Technology
基金 国家自然科学基金资助项目(62122070),信息探测与处理山西省重点实验室开放基金资助项目(2023-006)。
关键词 X射线成像 图像增强 梯度场 泊松分布 非局部均值 X-ray imaging image enhancement gradient field Poisson distribution non-local means
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