一种结合小波变换和维纳滤波的图像去噪算法

An Image Denoising Algorithm Combined with Wavelet Transform and Wiener Filtering

目的为了有效消除噪声图像中的椒盐噪声、高斯噪声甚至混合噪声,结合维纳滤波的优势和小波分解各分量的特点,提出一种新的图像去噪算法。方法该算法先将含噪声图像进行小波变换,分离出1个低频分量和3个中高频分量,然后对低频分量进行自适应维纳滤波,对3个中高频分量用Canny算子提取边缘,最后将处理后的4个分量进行重构得到去噪后的图像。结果仿真结果表明,该算法对扫描仪引入的常见噪声均表现出较好的去噪效果,PSNR值均大于20 d B。尤其是对于高斯噪声和混合噪声,新算法去噪后的PSNR结果高于维纳滤波、软阈值小波滤波和文献[9]算法1~8 d B,效果较好。结论结合小波变换和维纳滤波的图像去噪算法,能够较好去除噪声图像的多种类型噪声,是一种较为优秀的去噪算法。

In order to effectively eliminate salt ＆ pepper noise, Gaussian noise and even mixed noise in a noise image, a new image denoising algorithm was put forward based on the advantages of Wiener filtering and the features of all components of wavelet decomposition. This algorithm firstly conducted wavelet transform and separated 1 low frequency component and 3 medium ＆ high frequency components, and then carried out self-adaptive wiener filtering for the low frequency component. It then extracted edges of the 3 medium ＆ high frequency components by Canny operator, and finally reconstructed the 4 processed components and formed the de-noised image. Simulation results showed that the proposed algorithm had better performance in denoising common noise introduced by the scanner, with PSNR value more than 20 d B. Especially for Gaussian noise and mixed noise, it got relatively better PSNR value, 1~8 d B higher than Wiener filtering, soft threshold wavelet filtering and [9] algorithm. Combined with the wavelet transform and the Wiener filtering, this outstanding image denoising algorithm can better denoise various kinds of noise in noise images.