期刊信息
- 刊名： 河北师范大学学报（自然科学版）Journal of Hebei Normal University (Natural Science)
- 主办： 河北师范大学
- ISSN： 1000-5854
- CN： 13-1061/N
- 中国科技核心期刊
- 中国期刊方阵入选期刊
- 中国高校优秀科技期刊
- 华北优秀期刊
- 河北省优秀科技期刊
基于小波自适应阈值的脑CT图像去噪研究
- 河北医科大学 医学影像学院, 河北 石家庄 050017
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DOI：10.13763/j.cnki.jhebnu.nse.202101004
Denoising Research of Brain CT Based on the Adaptive Wavelet Thresholding Algorithm
摘要/Abstract
利用自适应小波阈值算法抑制脑部CT图像噪声，以提高图像质量.通过仿真实验，确定自适应滤波阈值与噪声强度的关系，然后采用真实脑部CT图像及自然图像进行验证，并与常用小波阈值算法比较.提出的算法能够抑制图像的加性白噪声，使峰值信噪比提高7~10dB，同时更好地保留了图像细节及边缘信息.小波自适应阈值算法能够对不同噪声水平的脑CT图像及自然图像进行自适应处理，提高峰值信噪比，改善图像质量.
In order to improve the image quality, the adaptive wavelet threshold algorithm is used to suppress the noise of brain CT image. The relationship between the adaptive filtering threshold and noise intensity is determined by simulation experiment. Then the real brain CT image and the natural image are used to demonstrate the validity of the proposed theory. Compared with the common wavelet threshold algorithm,the algorithm proposed in this paper,could not only suppress the additive white noise of the image effectively to enhance the SNR by 7~10dB, but also better preserve the image details and edge information. The wavelet adaptive threshold algorithm can process brain CT images and natural images with different noise levels, effectively suppress noise and improve image quality.
关键词
参考文献 12
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