去除脉冲噪声的小波阈值去噪算法

Wavelet Threshold Denoising Algorithm for Impulse Noise Removal

为了克服现有脉冲噪声去除算法的缺陷,进一步提升算法的去噪性能和鲁棒性,提出了一种去除脉冲噪声的小波阈值去噪算法。首先,根据脉冲噪声的灰度特征、分布的随机性及近似均匀性,用统计方法识别噪声像素。然后,用基于信噪强度的自适应阈值和可微收缩函数的小波去噪方法恢复噪声像素。实验结果表明,相比现有算法,本算法去噪得到的图像视觉感知效果、峰值信噪比和边缘保持指数均有较大提升,且具有更好的鲁棒性。

In order to address the deficiencies of existing algorithms for impulse noise removal,and to further improve denoising performance and robustness,a wavelet threshold denoising algorithm for impulse noise removal is proposed in this paper.First,based on the gray-scale characteristic of impulse noise,the randomness and approximate uniformity of its distribution,the noisy pixels are identified by using statistical method.Then,a wavelet denoising method based on an adaptive threshold of the signal-to-noise intensity and a differentiable shrinkage function is used to restore the noisy pixels.The experimental results show that,compared with the existing algorithms,the image visual perception effect,peak signal-to-noise ratio and edge preservation index obtained by the proposed algorithm are greatly improved,and it has better robustness.