基于粒子群优化的多小波图像降噪

Image Denoising in Multi-wavelet Domain Based on Particle Swarm Optimization

提出一种基于粒子群优化的多小波图像降噪方法。该方法首先根据图像降噪的特点,采用粒子群算法优化CL多小波的前置滤波器,实现了图像多小波变换的自适应预滤波;接着对一幅含噪声图像进行多小波分解,根据多小波分解后的能量分布特性,对小波系数进行阈值处理;后经多小波反变换,得到重构图像。实验表明,本文方法的客观性能(PSNR)和主观效果均优于传统小波去噪方法,同中值滤波和维纳滤波相比也有绝对优势。

An approach of image denoising in multi-wavelet domain based on particle swarm optimization was proposed. Firstly, particle swarm optimization was used to construct the adaptive pre-filters of CL multi-wavelet transform. Then noised image was decomposed by multi-wavelet transform and the coefficients were processed using threshold scheme according to the energy distribution of coefficients. Finally, denoised image could be obtained by inverse multi-wavelet transform. Experiments show, the proposed approach outperforms traditional wavelet denoising methods in terms of PSNR and visual effects. Moreover, the approach is beyond median filter and Wiener filter obviously.