摘要
数字图像处理对输入的图像有较高的要求,如果输入的图像噪声较大或者有干扰物,对图像特征的提取乃至后续的检测识别都有较大的影响。针对该问题,提出基于非下采样Shearlet变换的图像去噪算法,该算法采用非下采样Shearlet变换能够从多个尺度、多个方向上分解图像,从而更好地描述图像的轮廓、曲线等细节信息。利用阈值处理分解后的系数达到去噪效果。实验结果表明,该算法在去除图像噪声的同时能够很好地保留图像的细节,更有利于图像的检测。
In digital image processing, the requirements for the input image are high, and if there exists noise and interference in the input image, the feature extraction and the following detection and recognition will be inaccurate. To solve the problem, this paper proposed an image denoising algorithm based on non-subsampled shearlet transform. The algorithm used non-subsampled shearlet transform to decompose the image from multiple dimensions and directions, which could describe the detailed information better, such as outline and curve of the image. Besides, the algorithm utilized the coefficients after the threshold value decomposition, to achieve noise removal. The results showed that the algorithm could keep the detailed information of image after noise removal, which contributed to the image detection and recognition.
作者
陆焱
胡玉荣
LU Yah;HU Yurong(a.School of Computer Engineerin;b.Department of Science and Technology,Jingehu University of Teehnology,Jingmen 448000,Hubei,China)
出处
《江汉大学学报（自然科学版）》
2018年第6期513-521,共9页
Journal of Jianghan University：Natural Science Edition
基金
荆楚理工学院科研基金资助项目(YB201703)
荆门市科研计划资助项目(YDKY2017005)
作者简介
陆焱（1981-），女，讲师，硕士，研究方向：智能信息处理。;通讯作者：胡玉荣（1970-），女，教授，博士，研究方向：数据挖掘与智能计算。E-mail：641491521@qq．com
