曲线拟合确定阈值的非抽取小波贝叶斯图像去噪方法  

Undecimated Wavelet Bayesian Image Denoising Method with Its Threshold Determined by Curve Fitting

作　　者：王相海[1,2] 刘晓倩[2] 张爱迪[2] 傅博[1] 

机构地区：[1]辽宁师范大学计算机与信息技术学院,大连116029 [2]辽宁师范大学数学学院,大连116029

出　　处：《模式识别与人工智能》2016年第4期322-331,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61402214;41271422);教育部高等学校博士学科点专项科研基金项目(No.20132136110002);辽宁省博士科研启动基金项目(No.20121076);辽宁省教育厅科学研究项目(No.L2014423)资助~~

摘　　要：非抽取小波变换(UDWT)不仅具有时间和频率的局域特性,还具有良好的平移不变性,能有效抑制传统小波去噪方法产生的伪Gibbs现象.文中通过统计分析图像的UDWT系数,得到UDWT系数具有较强的非高斯统计特性的结论.在此基础上,应用广义高斯分布模型对系数进行建模,提出基于图像标准差的曲线拟合方法以提高图像噪声标准差估计值的精度,并以此确定去噪阈值.文中方法依据UDWT的平移不变特性有效抑制传统小波去噪方法出现的伪Gibbs现象,通过提高去噪阈值的精度以提高图像的去噪效果.大量仿真实验验证文中方法的有效性.The undecimated discrete wavelet transform（UDWT） possesses local features of time and frequency and shift-invariant property of reducing the pseudo-Gibbs phenomenon. In this paper, after the UDWT coefficients are analyzed, the conclusion that the UDWT coefficients have strong non-Gaussian statistical property is obtained. Grounded on the property, a generalized guassian distribution model is established. To improve the precision of standard deviation estimation of the noise image, a method of curve fitting is proposed based on the standard deviation of image, and thus the denoising threshold is determined. Based on the shift-invariant property of UDWT, the proposed method effectively reduces the pseudo-Gibbsphenomenon of the traditional wavelet denoising improving the accuracy of denoising threshold. effectiveness of the proposed method. method. Meanwhile, the denoising effect is enhanced by A large number of simulation experiments verifies the

关 键 词：非抽取小波变换(UDWT) 广义高斯分布 图像去噪 曲线拟合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]