采用结构自适应窗的非局部均值图像去噪算法

郝红侠 1; 刘芳 1; 2; 焦李成 2; 武杰 1; 2

doi:10.7652/xjtuxb201312013

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采用结构自适应窗的非局部均值图像去噪算法

更新时间：2025-07-09

- 采用结构自适应窗的非局部均值图像去噪算法
- A Non-Local Means Algorithm for Image Denoising Using Structure Adaptive Window
- 西安交通大学学报 2013年47卷第12期页码：71-76
- 作者机构：
  
  1. 西安电子科技大学计算机学院,西安,710071
  2. 智能信息感知与图像理解教育部重点实验室,西安,710071
- 作者简介：
- 基金信息：
  
  : 2013-06-15。作者简介: 郝红侠(1980—),女,博士生;刘芳(通信作者),女,教授,博士生导师。基金项目: 国家重点基础研究发展计划资助项目(2013CB329402);中央高校基本科研业务费专项资金资助项目(K5051203002,K5051203007);国家自然科学基金资助项目(61072106,61173090)
- DOI：10.7652/xjtuxb201312013
  中图分类号： TN911.73
- 网络首发：2013-12-10，
  
  纸质出版：2013
- 稿件说明：
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郝红侠 1, 刘芳 1, 2, 等. 采用结构自适应窗的非局部均值图像去噪算法[J]. 西安交通大学学报, 2013,47(12):71-76.

A Non-Local Means Algorithm for Image Denoising Using Structure Adaptive Window[J]. 2013, 47(12): 71-76.
郝红侠 1, 刘芳 1, 2, 等. 采用结构自适应窗的非局部均值图像去噪算法[J]. 西安交通大学学报, 2013,47(12):71-76. DOI： 10.7652/xjtuxb201312013.

A Non-Local Means Algorithm for Image Denoising Using Structure Adaptive Window[J]. 2013, 47(12): 71-76. DOI： 10.7652/xjtuxb201312013.

摘要

针对图像去噪处理中的非局部均值(NLM)算法相似性度量结果不够准确的问题

提出了一种采用结构自适应窗的非局部均值图像去噪(SAW-NLM)算法。首先利用从含噪图像中提取的初始素描图将含噪图像划分为结构区和非结构区

然后对这两部分区域分别采用基于结构方向的自适应窗和各向同性窗来搜索相似图像块

最后利用这些相似图像块得到当前待估计像素的去噪结果。为了抑制伪纹理现象

在估计过程中采用了块估计的方式。自适应窗有效结合了图像的结构方向和灰度信息

因此能够更准确度量图像块的相似性。实验结果表明:SAW-NLM算法具有更优的边缘保持和平滑效果

与传统NLM算法相比

峰值信噪比最大可提高1.1 dB

图像结构相似度也提高了4.6。

Abstract

A structure adaptive window based non-local means(SAW-NLM)algorithm for image denoising is proposed to solve the problem that the results of the similarity measurement are not accurate in the non-local means(NLM)method. The proposed algorithm divides a noisy image into two parts

structural area and non-structural area

by using the primal sketch map that is extracted from the noisy image. Then

similar samples are respectively searched in these two parts using structure direction based adaptive window and isotropic window. Finally

the denoising result is estimated from these samples. Moreover

the window-wise method is employed in the estimation of the interest to suppress the pseudo texture phenomenon. Since the structure direction and the gray information are jointly used in the adaptive window

the similarity can be more accurately measured. Experimental results show that the SAW-NLM algorithm has advantages in the edge preservation and smooth effects. Comparisons with the traditional NLM algorithm show that SAW-NLM method improves the peak signal-to-noise by 1.1 dB and the structural similarity index by 4.6.

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references

PERONA P, MALIK J. Scale-space and edge detection using anisotropic diffusion [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.

张闯, 迟健男, 张朝晖, 等. 基于边缘检测与双边滤波的彩色图像去噪 [J]. 电子学报, 2010, 38(8): 1776-1783.

ZHANG Chuang, CHI Jiannan, ZHANG Zhaohui, et al. Removing noise of color image based on edge detection and bilateral filter [J]. Acta Electronica Sinica, 2010, 38(8): 1776-1783.

JUMAH A A. Denoising of an image using discrete stationary wavelet transform and various thresholding techniques [J]. Journal of Signal and Information Processing, 2013, 4(1): 33-41.

BUADES A, COLL B, MOREL J M. Nonlocal image and movie denoising [J]. International Journal of Computer Vision, 2008, 76(2): 123-139.

COUPE P, YGER P, PRIMA S, et al. An optimized blockwise non-local means denoising filter for 3-D magnetic resonance images [J]. IEEE Transactions on Medical Imaging, 2008, 27(4): 425-441.

ZHONG Hua, CHENG Yang, ZHANG Xiaohua. A new weight for non-local means denoising using method noise [J]. Signal Processing Letters, 2012, 19(8): 535-538.

易子麟, 尹东, 胡安洲, 等. 基于非局部均值滤波的 SAR 图像去噪 [J]. 电子与信息学报, 2012, 34(4): 950-955.

YI Zilin, YIN Dong, HU Anzhou, et al. SAR image despeckling based on non-local means filter [J]. Journal of Electronics Information Technology, 2012, 34(4): 950-955.

GREENIG S, ZIMMERB S, WEICKERTA J. Rotationally invariant similarity measures for nonlocal image denoising [J]. Journal of Visual Communication and Image Representation, 2011, 22(2): 117-130.

GUO Chengen, ZHU Songchun, WU Yingnian. Primal sketch: integrating texture and structure [J]. Computer Vision and Image Understanding, 2007, 106(1): 5-19.

CHENG Hengda, XUE Mei, SHI Xiangjin. Contrast enhancement based on a novel homogeneity measurement [J]. Pattern Recognition, 2003, 36(11): 2687-2697.

朱磊, 水鹏朗, 章为川, 等. 利用区域划分的合成孔径雷达图像相干斑抑制算法 [J]. 西安交通大学学报, 2012, 46(10): 83-89.

ZHU Lei, SHUI Penglang, ZHANG Weichuan, et al. A despeckling algorithm for synthetic aperture radar image using region subdivision [J]. Journal of Xi'an Jiaotong University, 2012, 46(10): 83-89.

LI H C, FAN P Z, KHAN M K. Context-adaptive anisotropic diffusion for image denoising [J]. Electronics Letters, 2012, 48(14): 827-829.

DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering [J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095.

WANG Zhou, BONIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

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