摘要:
针对现有去噪算法去噪不彻底、噪声误判、损害图像边缘和纹理细节信息的缺点,提出一种联合双边滤波器和小波阈值收缩图像去噪算法.首先,使用双边滤波器对含有噪声图像进行分层;其次,对不同分层结果,选择不同滤波器进行去噪:高对比度层采用双边滤波器,低对比度层采用小波阈值收缩去噪方法;最后,融合高、低对比度层去噪图像,实现有效去除噪声的同时,保证图像信息完整.实验结果表明,本文算法的峰值信噪比达到40.99 dB,比非局部均值滤波、双边滤波器、小波阈值收缩和偏微分方程图像去噪算法分别提高了7.79%,3.56%,11.22%和1.91%;与此同时,还能有效保留图像边缘和纹理等细节信息.
Abstract:
Aiming at overcoming the shortcomings of existing denoising algorithms, such as the poor denoising capability,the noise error evaluation,and the damaging of the image edge and texture details,this paper proposes an image denoising algorithm of joint bilateral filter and wavelet threshold shrinkage. Firstly, the original noise image is divided into high -contrast and low -contrast layers by bilateral filter. Secondly, different appropriate filters are employed for different hierarchical layers. i.e., the bilateral filter and wavelet threshold shrinkage are adopted for high-contrast and low-contrast layers,respectively. Finally,the final denoising image is obtained by integrating high-contrast with low-contrast layers' denoising images, which suppresses noises and at the same time enhances the image more efficiently. Experimental results show that peak signal to noise ratio(PSNR) of this method reaches 40.99 dB, which is higher than the ratio of non -local means filter, bilateral filter, wavelet threshold shrinkage and partial differential equation algorithms by 7.79%, 3.56%, 11.22% and 1.91%, respectively. Moreover, the proposed algorithm can not only remove the noises efficiently but also preserve the image edge and texture details very well.
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