一种基于双树复小波变换的图像去噪算法
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摘要
针对图像处理的需求,提出一种基于双树复小波变换的图像去噪算法。该算法对图像进行双树复小波变换,对变换后的系数利用最大似然估计和维纳滤波进行去噪,采用最大后验概率来估计双树复小波的方差,利用维纳滤波得到去噪后的系数,通过双树复小波反变换得到去噪后的图像。在分解层计算方差时,均采用在4×4的结构元素内基于最大值和次大值坍缩后的邻域来计算方差。实验结果表明,该算法的PSNR对比其他文献提高0.2 d B左右,运行时间减少5 s。
An algorithm for image denoising based on double-tree complex wavelet transform is proposed. The algorithm firstly applies double-tree complex wavelet transform to the images,and denoises the transformed coefficients using maximum likelihood estimation and Wiener filtering.The maximum posterior probability is used to estimate the variance of the double-tree complex wavelet,and Wiener filtering to get the denoised coefficient,finally the denoised image can be obtained through the double-tree complex wavelet inverse transform.The neighborhood in the 4 × 4 structural elements based on the maximal and the submaximal collapses is used to calculate the variance in the decomposition layer. The experimental results prove that the PSNR is improved by about 0.2 d B,and the operation time reduced by 5 s,as compared with other algorithms.
An algorithm for image denoising based on double-tree complex wavelet transform is proposed. The algorithm firstly applies double-tree complex wavelet transform to the images,and denoises the transformed coefficients using maximum likelihood estimation and Wiener filtering.The maximum posterior probability is used to estimate the variance of the double-tree complex wavelet,and Wiener filtering to get the denoised coefficient,finally the denoised image can be obtained through the double-tree complex wavelet inverse transform.The neighborhood in the 4 × 4 structural elements based on the maximal and the submaximal collapses is used to calculate the variance in the decomposition layer. The experimental results prove that the PSNR is improved by about 0.2 d B,and the operation time reduced by 5 s,as compared with other algorithms.
引文
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[3]余金澳,吴彦鸿.一种改进的基于小波变换的相干斑去噪方法[J].无线电工程,2017,47(5):87-90.
[4]周小军,郭佳,周承仙,等.基于改进同态滤波的遥感图像去云算法[J].无线电工程,2015,45(3):14-18.
[5]李红延,周云龙,田峰,等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200-2206.
[6]徐源浩.多尺度融合理论的研究与应用[D].西安:西安电子科技大学,2014.
[7]刘蕾.基于双树复小波变换的图像去噪[D].北京:北京化工大学,2010.
[8]陈广秋,高印寒,刘妍妍.双密度双树复小波域多聚焦图像融合[J].计算机工程与应用,2013,49(10):180-183.
[9]李丹,柏桐,庞宇,等.基于双树复小波变换和形态滤波的PPG信号去噪方法[J].生命科学仪器,2016,14(Z2):54-56.
[10]殷明,白瑞峰,邢燕,等.基于非下采样双树复小波域的双变量模型去噪算法[J].光子学报,2014,43(10):137-143.
[11]刘玉淑.基于多尺度变换的图像去噪及融合算法研究[D].济南:山东大学,2013.
[12]罗鹏,高协平.基于双树复数小波变换的图像去噪方法[J].光子学报,2008(3):604-608.
[13]黄学优,张长江.双树复小波域的MRI图像去噪[J].中国图像图形学报,2016,21(1):104-113.
[14] NIELSEN M A,CHUANG I L.量子计算和量子信息[M].赵千川,译.北京:清华大学出版社,2004.
[15]毕思文.量子遥感的概念、框架与内涵研究探索[J].红外与毫米波学报,2003,22:1-9.
[16]张莹.基于量子衍生的遥感图像处理研究[D].北京:中国科学院大学,2015.
[17]王娟.应用量子线路计算超图Ramsey数[D].天津:天津大学,2013.
[18]王相海,李放,王爽.小波方向子带偏微分方程遥感图像去噪[J].中国图象图形学报,2012,17(5):721-728.
[19] AYATOLLAHI,RAIE A A,HAJATI F. Expressioninvariant Face Recognition Using Depth and Intensity Dual-tree Complex Wavelet Transform Features[J]. Electronic Imaging,2015,24(2):23-31.
[20]许悟生.基于量子理论的数字图像处理研究[D].长沙:湖南师范大学,2013.