基于改进的小波阈值图像去噪算法

Image denoising algorithm based on improved wavelet threshold

为了克服传统小波阈值函数存在固定偏差和不连续的问题,提出了一种改进的小波阈值函数,将改进的小波阈值方法和软阈值、硬阈值、Birge-Massart策略软阈值、半软阈值、半软阈值组合均值滤波等方法比较,峰值信噪比提高了1.2%~6.9%,均方根误差减小了4.8%~22.4%.在混有高斯噪声、椒盐噪声和斑点噪声的图像中,改进的小波阈值函数和自适应中值滤波组合的方法较单一的自适应中值滤波,峰值信噪比提高3 dB,均方根误差减小27%.结果表明,该算法有效的提升了高斯噪声图像的去噪效果,而且组合去噪方法对图像中的混合噪声去除效果较好.

In order to overcome the problems of fixed deviation and discontinuity of the traditional wavelet threshold function,an improved wavelet threshold function is proposed.Compared with soft threshold method,hard threshold method,Birge MassArt strategy soft threshold method,half soft threshold method and half soft threshold combined mean filtering method,the peak signal-to-noise ratio is increased by 1.2%~6.9%,and the root mean square error is reduced by 4.8%~22.4%.In the image mixed with Gaussian noise,salt and pepper noise and speckle noise,the combination method of improved wavelet threshold function and adaptive median filter is better than the single adaptive median filter,the peak signal-to-noise ratio is increased by 3 dB,and the root mean square error is reduced by 27%.The results show that the algorithm effectively improves the denoising effect of Gaussian noise image,and the combined denoising method has a better effect on the removal of mixed noise in the image.