基于离散平稳小波的影像恢复去噪方法  

Image recovery denoising method based on stationary wavelet transform

作　　者：王昶[1] 郭东升 WANG Chang;GUO Dongsheng(School of Civil Engineering,University of Science and Technology Liaoning,Anshan Liaoning 114051,China;Tieling Natural Resources Affairs Service Center,Tieling Liaoning 112008,China)

机构地区：[1]辽宁科技大学土木工程学院,辽宁鞍山114051 [2]铁岭市自然资源事物服务中心,辽宁铁岭112008

出　　处：《北京测绘》2022年第11期1557-1563,共7页Beijing Surveying and Mapping

摘　　要：为了避免图像去噪过程中图像边缘细节信息大量丢失、去噪图像出现模糊及阶梯效应等问题,本文提出一种基于离散平稳小波(SWT)的图像恢复去噪算法。首先,对噪声遥感图像进行3层SWT分解;其次,构建图像恢复去噪算法,并采用此算法对每一层的高频分量进行去噪处理;最后,对低频分量和去噪后的高频分量进行SWT重构,获得去噪图像。实验结果表明,本文提出的图像去噪算法不仅可以有效地抑制图像的随机噪声,而且能在去除图像随机噪声的同时保留更多图像的边缘细节信息,去噪图像呈现较好的视觉效果。In order to avoid the large loss of edge detail information of image、de-noising image blur and generate step effect in image stationary region in the process of image de-noising,this paper proposed an image restoration de-noising algorithm based on stationary wavelet transform.Firstly,in order to effectively separate the edge detail information form noise remote sensing image,we performed three layers decomposition by stationary wavelet transform(SWT)for the noise remote sensing image;Secondly,in order to achieve the aim of effectively removing noise of the high frequency component while retaining more edge detail information of the image,we utilized image restoration algorithm to implement noise-elimination in the high frequency components of each layer.Finally,we obtained the de-noising image by reconstructing the low frequency and the high frequency component obtained by image restoration algorithm.Experimental results demonstrated that the proposed method could not only effectively suppress the noise,but also maintained more the edges of the image as much as possible while suppressing the image noise.In conclusion,the de-noising image presents a better visual effect.

关 键 词：离散平稳小波 图像恢复算法 影像噪声 

分 类 号：P237[天文地球—摄影测量与遥感]