基于LoG算子改进的自适应阈值小波去噪算法

董雪; 林志贤; 郭太良

doi:10.3788/YJYXS20142902.0275

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基于LoG算子改进的自适应阈值小波去噪算法

成像技术与图像处理 | 更新时间：2020-08-12

- 基于LoG算子改进的自适应阈值小波去噪算法
- Improved self-adaptive threshold wavelet denoising analysis based on LoG operator
- 液晶与显示 2014年29卷第2期页码：275-280
- 作者机构：
  
  福州大学物理与信息工程学院,福建福州,350002
- 作者简介：
- 基金信息：
  
  国家863重大专项（No.2012AA03A301，2013AA030601）();国家自然科学基金（No.61101169，No.61106053）();福建省自然科
- DOI：10.3788/YJYXS20142902.0275
  中图分类号： TP394.1;TH691.9
- 收稿日期：2013-09-13，
  
  修回日期：2013-10-12，
  
  纸质出版日期：2014-03-05
- 稿件说明：
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董雪, 林志贤, 郭太良. 基于LoG算子改进的自适应阈值小波去噪算法[J]. 液晶与显示, 2014,29(2): 275-280

DONG Xue, LIN Zhi-xian, GUO Tai-liang. Improved self-adaptive threshold wavelet denoising analysis based on LoG operator[J]. , 2014,29(2): 275-280
董雪, 林志贤, 郭太良. 基于LoG算子改进的自适应阈值小波去噪算法[J]. 液晶与显示, 2014,29(2): 275-280 DOI： 10.3788/YJYXS20142902.0275.

DONG Xue, LIN Zhi-xian, GUO Tai-liang. Improved self-adaptive threshold wavelet denoising analysis based on LoG operator[J]. , 2014,29(2): 275-280 DOI： 10.3788/YJYXS20142902.0275.

摘要

图像在传输过程中会受到各种噪声干扰，为了实现消除噪声的目的，提出一种基于LoG算子改进的自适应阈值去噪算法。首先，利用LoG算子提取图像的边缘特征信息。接着，根据图像的边缘特征和非边缘特征分别求取改进的阈值函数：对于图像非边缘部分的阈值函数，在软阈值函数的基础上添加一个阈值修正系数，构建新的阈值函数；对于图像边缘部分的阈值函数，将边缘部分小波系数附近的能量和阈值相结合，构建新的阈值函数。最后利用改进的阈值函数对图像R、G、B 3个通道分别处理，保留图像所有的细节信息。实验结果表明，消噪后图像与含噪图像的PSNR值高于传统自适应算法12.09%；MAE值低于传统自适应算法22%。该算法有效保存了图像的边缘信息，综合去噪效果明显提高。

Abstract

The image will be affected by various noise interference in the process.In order to eliminate the noise of the image

an improved self-adaptive threshold wavelet denoising analysis based on LoG operator was proposed.First

the image edge character information was extracted by LoG operator.Then

according to the edge of the image features and the no-edge of the image features

the improved threshold function was calculated respectively:on the basis of soft-thresholding function

a modified threshold coefficient was added for the threshold function in the image no-edge areas;in the no-edge areas

a new threshold function was built combining the energy near the wavelet coefficients with threshold. Finally

dealing with the red

green and blue(R

B) channels separately by the improved threshold function

all the details of the image were reserved.Experimental results show that PSNR of the image was above 12.09% compared with the traditional adaptive algorithm;MAE of the image was below 22% compared with the traditional adaptive algorithm.The image of this algorithm has a better quality than others

keeping the edge's information effectively

the integrative denoising is improved obviously.

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references

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