摘要
为了解决传统软、硬阈值算法去噪过程中出现的边缘模糊和视觉失真等问题,提出了一种新的小波去噪算法。首先,在阈值选择时,为了抑制信号长度对阈值选取的影响,把通用阈值和最大似然比按照一定比例结合,可根据图像的噪声构造出更加合理的阈值;然后,阈值函数里加入了调节系数β,使新的函数拥有更好的连续性和更低的误差。仿真实验表明,该算法是有效的,可行的,去噪后的图像相比于其他三种经典算法拥有更高的信噪比和更低的均方误差,能更好的去除图像中的噪声。
In order to solve the problems of edge blur and visual distortion in the traditional soft and hard threshold algorithms,a new wavelet denoising algorithm is proposed.Firstly,in order to suppress the influence of signal length on the threshold selection,a more reasonable threshold can be constructed according to the noise of the image by combining the general threshold with the maximum likelihood ratio according to a certain proportion.Then,the adjustment coefficient is added to the threshold function to make the new function have better continuity and lower error.Simulation results show that the algorithm is effective and feasible.Compared with the other three classical algorithms,the denoised image has higher signal-to-noise ratio and lower mean square error.
作者
孙然然
林玉娥
梁兴柱
SUN Ranran;LIN Yue;LIANG Xingzhu(School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China;Institute of Environment-friendly Materials and Occupational Health (Wuhu), Anhui University of Science and Technology, Wuhu Anhui 241003, China)
出处
《佳木斯大学学报（自然科学版）》
CAS
2021年第2期63-66,共4页
Journal of Jiamusi University：Natural Science Edition
基金
安徽省教育厅自然科学研究项目(KJ2018A0084)
芜湖市科技计划项目(2020yf48)。
关键词
阈值函数
小波去噪
信噪比
均方误差
threshold function
wavelet denoising
signal to noise ratio
mean square error
