Non-local Mean Denoising Algorithm Based on Local Hu Moment

doi:10.3969/j.issn.1000-3428.2018.03.040

Abstract

Abstract: Aiming at the problem that the similarity between neighborhood blocks in non-local means is not accurate,a local Hu moment denoising algorithm based on Krawtchouk polynomial weighting function is proposed.By combining the weighting function of Krawtchouk polynomial and image function,a geometric moment new weighting function is constructed.The new center moment is obtained by using the constructed geometrical moment weighting function.A set of featurevectors are constructed by using two moment invariants with second order and third order center moments.The Euclidean distance is used to measure the similarity of the feature vectors in the neighborhood and the new weighting is obtained by combining the weighting with the neighborhood blocks.Experimental results under different intensities of noise,show that compared with the original non-local mean noise denoising algorithm,the peak signal to noise ratio and structural similarity are significantly improved.

Key words: non-local mean, Hu invariant moment, measure, feature value, weighting, Krawtchouk polynomials

摘要： 针对非局部均值中度量邻域块间相似性不够准确的问题,提出一种基于Krawtchouk多项式权重函数的局部Hu矩的去噪算法。将Krawtchouk多项式的权重函数与图像函数相结合构造几何矩的新的权重函数。利用几何矩权重函数得到新的中心矩。使用二阶和三阶中心矩构造7个不变矩组成特征矢量,通过欧式距离度量邻域间特征矢量的相似性,并与邻域块间的权重相结合得到新的权重。在不同噪声强度下的测试结果表明,与原始非局部均值去噪算法相比,该算法峰值信噪比与结构相似度都有明显提高。

关键词: 非局部均值, Hu不变矩, 度量, 特征值, 权重, Krawtchouk多项式

CLC Number:

TP391

ZOU Binyi,LIU Hui,SHANG Zhenhong,LI Runxin. Non-local Mean Denoising Algorithm Based on Local Hu Moment[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2018.03.040.

邹滨益,刘辉,尚振宏,李润鑫. 基于局部Hu矩的非局部均值去噪算法[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2018.03.040.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.3969/j.issn.1000-3428.2018.03.040

http://www.ecice06.com/EN/Y2018/V44/I3/241

References

参考文献［1］余丽红,冯衍秋,陈武凡.基于自适应正则化的全变分去噪算法［J］.中国图象图形学报,2009,14(10):1950-1954. ［2］天沛,李庆周,马平,等.一种基于小波变换的图像去噪新方法［J］.中国图象图形学报,2008,13(3):394-399. ［3］王新楼,乔明,邹谋炎.一种基于偏微分方程的SAR图像去噪方法［J］.电子与信息学报,2005,27(9):1365-1369. ［4］BUADES A,CCOLL B,MOREL J M.A Non-local Algorithm for Image Denoising［C］//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2005:60-65. ［5］MAHMOUDI M,SAPIRO G.Fast Image and Video Denoising via Nonlocal Means of Similar Neigh-borhoods［J］.IEEE Signal Processing Letters,2005,12(12):839-842. ［6］COUPE P,YGER P,PRIMA S,et a.An Optimized Blockwise Non-local Means Denoising Filter for 3D Magnetic Resonance Images［J］.IEEE Transactions on Medical Imaging,2008,27(4):425-441. ［7］刘晓明,田雨,何徽,等.一种改进的非局部均值图像去噪算法［J］.计算机工程,2012,38(4):199-201. ［8］韩震,王红斌,余正涛,等.双边非局部均值滤波图像去噪算法［J］.传感器与微系统,2016,33(6):124-131. ［9］WU Yiquan,DAI Yimian.Fast Non-local Means Algorithm Based on Krawtchouk Moments［J］.Journal of Tianjin University,2015,21(2):104-112. ［10］SIT A,KIHARA D.Comparison of Image Patches Using Local Moment Invariants［J］.IEEE Transactions on Image Processing,2014,23(5):2369-2379. ［11］ZHONG Hua,YANG Chen,ZHANG Xiaohua.A New Weight for Nonlocal Means Denoising Using Method Noise［J］.IEEE Signal Processing Letters,2012,19(8):535-538. ［12］DELEDALL C A,DUVAL V,SALMON J.Non-local Methods with Shape Adaptive Patches［J］.Journal of Mathematical Imaging and Vision,2012,43(2):103-120. ［13］ZENG W L,LU X B.Region-based Non-local Means Algorithm for Noise Removal［J］.Electronics Letters,2011,47(20):1125-1127. ［14］BUADES A,COLL B,MOREL J M.A Review of Image Denoising Algorithms,with a New One［J］.SIAM Journal on Multiscale Modeling and Simulation,2005,4(2):490-530. ［15］HU M.Visual Pattern Recognition by Moment Invariants［J］.IRE Transactions on Information Theory,1962,8(2):179-187. 编辑索书志

[1]	Weifen TANG, Cuifang GAO. Dynamic Time Warping Algorithm with Adaptive Weighting of Extreme Points [J]. Computer Engineering, 2023, 49(7): 150-160.
[2]	WANG Xindi, YANG Su, ZHANG Siyuan, LUO Wuyang, LI Jie, LIU Hui. Urban Fire Risk Prediction Based on Spatial-Temporal Big Data and Satellite Images [J]. Computer Engineering, 2023, 49(6): 242-249.
[3]	JIN Haibo, ZHAO Xinyue. High-Reliability Intrusion Detection Algorithm Under Conformal Prediction Framework [J]. Computer Engineering, 2022, 48(7): 130-140.
[4]	YE Heyuan, HAN Li, SUN Shimin. Network Measurement Node Selection Algorithm Based on Ant Colony Optimization in SDN [J]. Computer Engineering, 2022, 48(5): 191-199.
[5]	WANG Zhengkai, SHEN Dongsheng, WANG Chenxi. Fisher Score Fast Multi-Label Feature Selection Algorithm Based on Text Classification [J]. Computer Engineering, 2022, 48(2): 113-124.
[6]	LIU Yuhong, CHEN Manyin, LIU Xiaoyan. Multi-Scale Fully Convolutional Compressed Sensing Reconstruction Based on Channel Attention [J]. Computer Engineering, 2022, 48(12): 189-195.
[7]	LU Yi, WANG Peng, WANG Wei. Time-Series Semantic Mining Algorithm Based on Sub-Series Similarity [J]. Computer Engineering, 2022, 48(10): 88-94.
[8]	YAN Tianran, MA Xiaojing, RAO Yinglu, DU Yanli, MA Sile. Rebar Size Detection Algorithm for Intelligent Construction Supervision Based on Improved Mask R-CNN [J]. Computer Engineering, 2021, 47(9): 274-281.
[9]	LIU Miaomiao, ZHOU Conghua, ZHANG Ting. DTW Similarity Measure Based on Segmentation Features and Adaptive Weighting [J]. Computer Engineering, 2021, 47(8): 62-68,77.
[10]	WANG Hai, WENG Chenao, LI Ke, LUO Xi. An Improved SVM Algorithm for Azimuth Estimation of Base Station Sector [J]. Computer Engineering, 2021, 47(4): 120-126.
[11]	YAO Yujie, LIU Guangzhong, KONG Weiquan. Time Synchronization Algorithm of Underwater Sensor Based on Doppler Velocity Measurement [J]. Computer Engineering, 2021, 47(4): 147-152.
[12]	JIN Guoqiang, CHEN Yuxuan, KAN Yan, JIN Yi, ZHU Chang'an. Attitude Measurement Method Based on Inertia Ellipse Fitting for the Small Targets [J]. Computer Engineering, 2021, 47(4): 211-217,225.
[13]	TANG Yu, XUE Zhishuang, LIU Xiaofang, LIU Yongchun, ZHANG Guo, YU Liang. Attitude Consistency Control of Multiple UAVs Based on Fault Observer [J]. Computer Engineering, 2021, 47(3): 311-320.
[14]	YE Meng, YANG Juan, WANG Ronggui, XUE Lixia, LI Dong. Small Sample Learning Method Based on Feature Aggregation Network [J]. Computer Engineering, 2021, 47(3): 77-82.
[15]	XIA Hansong, ZHANG Lisheng, SANG Chunyan. Improved Algorithm of Dynamic Time Warping Based on LDTW [J]. Computer Engineering, 2021, 47(11): 108-120.

Please choose a citation manager

Content to export