作　　者： ;

机构地区： 泉州师范学院物理与信息工程学院

出　　处： 《泉州师范学院学报》 2014年第2期67-70,共4页

摘　　要： 针对图像中的高斯噪声干扰,提出一种改进的图像去噪方法.首先利用Curvelet变换将含噪声图像分解成多个子频带,再根据子带系数的高斯分布特性,利用阈值去噪和加权平均滤波相结合的方法对高频子带进行去噪处理,最后利用Curvelet反变换得到去噪后的图像.为了验证该方法的有效性,与传统的硬阈值、软阈值、基于小波变换的方法相比较,实验结果表明,该方法能够获得较好的峰值信噪比和视觉特性,保留较多的细节信息.同时也说明了Curvelet变换比小波变换能够得到更好的去噪效果. The purpose of this paper is to study a method of de-noising of images corrupted with additive white Gaussian noise. Firstly, the noisy image is decomposed into many levels to obtain different frequency sub-bands by Curvelet transform. Secondly, the threshold estimation and the weighted average method are used to remove the noisy coefficients according to generalized Gaussian distribution modeling of sub-band coefficients, Ultimately,invert the multi-scale decomposition to reconstruct the de-noised image.Here,to prove the performance of the proposed method,the results are compared with other existent algorithms such as hard and soft threshold based on wavelet. The simulation results on several testing images indicate that the proposed method outperforms the other methods in peak signal to noise ratio and keeps better visual in edges information reservation as well. The results also suggest that Curvelet transform can achieve a better performance than the wavelet transform in image de-noising.

关 键 词： 变换 阈值 图像去噪 加权平均滤波 小波变换

领　　域： [生物学]