全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于改进的邻域平均滤波方法的X射线图像去噪
X-Ray Image Denoising Based on Improved Neighborhood Average Filtering Method

DOI: 10.12677/JISP.2023.121001, PP. 1-8

Keywords: X射线图像,改进的邻域平均滤波,噪声检测,图像去噪,峰值信噪比
X-Ray Image
, Improved Neighborhood Averaging Filtering, Noise Detection, Image Denoising, Peak Signal-to-Noise Ratio

Full-Text   Cite this paper   Add to My Lib

Abstract:

为了获得更清晰的X射线图像,在去噪的同时尽可能保护边界完整,针对传统的均值滤波去噪时造成边缘模糊的问题,提出了一种改进的邻域平均滤波去噪方法。对图像中的未知噪声进行检测分析,确定噪声类型和参数;使用改进的邻域平均滤波方法对图像去噪,对滤波模板邻域内像素与中心像素做差值,针对不同类型噪声,选择不同数量的邻域内对应差值较小的几个像素取平均值替代中心像素,避免不同区域像素被混叠处理造成边界模糊;计算去噪后X射线图像的均方误差和峰值信噪比,对去噪效果进行客观评价,并将提出的去噪方法与常见的滤波方法进行比较。实验结果表明,改进的邻域平均滤波去噪方法相比常见的滤波方法在去噪的同时能够更好地保护边界。
In order to obtain a clearer X-ray image and protect the boundary integrity as much as possible while denoising, an improved denoising method by neighborhood mean filtering is proposed to solve the problem of blurred edges caused by traditional mean filtering. We detect and analyze the unknown noise in the image, determine the noise type and parameters; use the improved neighborhood average filtering method to denoise the image, and make the difference between the pixels in the neighborhood of the filter template and the center pixel, and choose different values for different types of noise. The average value of several pixels with small corresponding differences in a number of neighborhoods is used to replace the center pixel, so as to avoid the blurring of boundaries caused by the aliasing of pixels in different regions; the mean square error and peak signal-to-noise ratio of the denoised X-ray images are calculated, the denoising effect is objectively evaluated, and the proposed denoising method is compared with the common filtering methods. The experimental results show that the improved neighborhood average filtering denoising method can better protect the boundary while denoising than the common filtering method.

References

[1]  徐道磊. 基于X射线成像的铸件缺陷检测系统及噪声干扰去除[D]: [硕士学位论文]. 广州: 华南理工大学, 2013.
[2]  Khan, S. and Lee, D.-H. (2017) An Adaptive Dynamically Weighted Median Filter for Impulse Noise Removal. EURASIP Journal on Advances in Signal Processing, 1, 67.
https://doi.org/10.1186/s13634-017-0502-z
[3]  李珅. 基于稀疏表示的图像去噪和超分辨率重建研究[D]: [博士学位论文]. 西安: 中国科学院研究生院(西安光学精密机械研究所), 2014.
[4]  王小兵, 孙久运, 汤海燕. 基于小波变换的图像混合噪声自适应滤波算法[J]. 微电子学与计算机, 2012, 29(6): 91-95.
[5]  杨希, 杨立瑞. 基于混合滤波器的X射线液体图像滤波算法[J]. 核电子学与探测技术, 2013, 33(1): 85-90+98.
[6]  张旭涛. 基于高斯-椒盐噪声的滤波算法[J]. 计算机科学, 2019, 46(S1): 263-265.
[7]  袁健, 姜振宇, 石凌峰. 基于非局部自相似性的混合噪声滤波算法[J]. 光学技术, 2020, 46(3): 361-367.
[8]  高东生, 廖泓舟, 王侃, 代翔. 一种适用于椒盐-高斯干扰信号的自适应滤波改进算法[J]. 电讯技术, 2021, 61(12): 1554-1561.
[9]  吴翰. 数字图像的高斯噪声去噪算法研究[D]: [硕士学位论文]. 安庆: 安庆师范大学, 2018.
[10]  张英. 一种基于高斯与椒盐混合噪声去噪算法研究[D]: [硕士学位论文]. 西安: 西安科技大学, 2014.
[11]  刘笃晋. 基于小波变换的图像去噪方法研究[J]. 现代电子技术, 2013, 7(3): 94-95.
[12]  赵存. X射线数字成像系统设计及成像技术研究[D]: [硕士学位论文]. 杭州: 杭州电子科技大学, 2017.
[13]  佟雨兵, 张其善, 祁云平. 基于PSNR与SSIM联合的图像质量评价模型[J]. 中国图象图形学报, 2006, 11(12): 1758-1763.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413