基于旋转、平移和尺度不变的平稳小波图像去噪  

Image de-noise based on the stationary wavelet translation with rotation,shift and scale invariance

作　　者：李晋炬[1] 马志峰[1] 吴琼之[1] 杜娟[1] 

机构地区：[1]北京理工大学信息与电子学院,北京100081

出　　处：《激光与红外》2010年第11期1263-1268,共6页Laser & Infrared

摘　　要：针对传统的离散正交小波变换对信号的起始位置比较敏感的特点,提出了具有旋转、平移和尺度不变的平稳小波变换,将图像变换到极坐标,采用方向能量函数确定图像主轴方位,并将图像主轴旋转到水平方向得到方向归一化的图像。然后通过对图像的重整和小波基的位移、伸缩、旋转,来消除位移和尺度的影响。采用基于Bayesian自适应阈值估计的方法,通过最小化Bayesian风险函数获得具有最大信噪比的图像近似最优消噪阈值,提出一种利用输入数据直接得到渐近最优阈值的图像去噪方法。实验结果表明,所提出的方法能够在去除噪声的同时很好地保留图像的边缘,是一种有效的图像去噪方法。Since traditional discrete orthonormal wavelet transform being more sensitive to the original position of signal.In the paper,we propose a stationary wavelet transform which possess the properties of rotation,shift and scale invariance.First transforming the image to polar coordinates,and confirming the principal axis orientation to adopt orientation energy function,then rotating the principal axis of image to the level orientation to gain the unifying image in the orientation.By reforming the image and rotation,shift and invariance to the wavelet bases,thus we can eliminate the influence of shift and scale.In the paper,we use Bayesian adaptive threshold estimation to gain the approximate optimization de-noising threshold.The image possesses the most PSNR.Thus we propose a kind of method which uses the input data to gain the asymptotic optimization de-noising threshold.Finally,experimental result show that the method can better reserve the image edge and de-noise at the same time,and being a kind of valid method of image de-noising.

关 键 词：平稳小波 图像去噪 Bayesian阈值估计 峰值信噪比 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]