基于非局部的图像去噪方法的发展现状综述

摘要/Abstract

摘要：

通过对图像的相似块做处理从而达到去噪目的,是当前较为新颖的去噪方法,在对国内外利用相似块去噪文献进行理解和分析的基础上,回顾相似块去噪研究的发展,阐明相似块去噪的原理,并总结了相似块去噪的各种方法。最终基于对相似块去噪方法的分析,提出了对其研究方法的展望。

关键词: 图像去噪, 非局部平均, 相似块, 小波去噪, 低秩逼近

Abstract:

Image denoising by non-local means has been well acknowledged as an important approach to image denoising in recent years.This paper presents an overview of sparse model denoising based on Non-local means denoising research and analysis of recent domestic and abroad literatures.To begin with,this paper reviews the development of Non-local means research,then summarizes several methods in procedure of Non-local means denoising,and finally points out the problems and future directions of Non-local means denoising method.

Key words: image denoising;non-local mean;similar patch;wavelet denoising;low-rank approach

中图分类号:

TP391.41

翟栋, 丁亚男, 李涛. 基于非局部的图像去噪方法的发展现状综述[J]. , 2014, 27(1): 160-.

ZHAI Dong, DING Ya-Nan, LI Tao. An Overview of Non-local Image Denoising[J]. , 2014, 27(1): 160-.

参考文献

［1］钟锦敏.非局部平均的去噪方法研究［D］.上海:上海交通大学,2007.

［2］BUADES A,COLL B,MOREL J M.A non-local algorithm for image denoising［C］.IEEE Int.Conf.on Computer Vision and Pattern Recognition,CVPR 2005,2005(2):60-65.

［3］BUADES A,COLL B,MOREL J M.A review of image denoising algorithms,with a new one［J］.Multiscale Modeling Simulation,2005,4(2):490-530.

［4］ZHANG L,DONG W,ZHANG D,et al.Two-stage image denoising by principal component analysis with local pixel grouping Pattern Recognition［J］.Pattern Recognition,2010,43(4):1531-1549.

［5］AJIT R,ANAND R,ARUNAVA B.Image denoising using the higher order singular value decomposition［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(4):849-962.

［6］KERVRANN C,BOULANGER J.Optimal spatial adaptation for patch-based image denoising［J］.IEEE Transactions on Image Process,2006,15(10):2866-2878.

［7］DABOV K,FOI A,EGIAZARIAN K.Video denoising by sparse 3d transform-domain collaborative filtering［C］.InProc.15th European Signal Processing Conference,2007.

［8］AHARON M,ELAD M,BRUCKSTEIN K.K-SVD:An algorithm for designing overcompletes dictionaries for sparse representation［J］.IEEE Transactions on Signal Process,2006,54(11):4311-4322.

［9］CHATTERJEE P,MILANFAR P.Clustering-based denoising with locally learned dictionaries［J］.IEEE Transactions on Img.Proc,2009,18(7):1438-1451.

［10］肖泉,丁兴号,王守觉,等.基于自适应超完备稀疏表示的图像去噪方法［J］.仪器仪表学报,2009(9):1886-1890.

［11］ZHOU M,CHEN H,PAISLEY J,et al.Nonparametric bayesian dictionary learning for analysis of noisy and incomplete images［J］.IEEE Transactions on Image Processing,2012,21(1):130-144.

［12］JI Hui,LIU Chaoqiang,SHEN Zuowei,et al.Robust video denoising using low rank matrix completion［C］.San francisco:IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2010.

［13］LIU Guangcan,LIN Zhouchen,YAN Shuicheng,et al.Robust recovery of subspace structures by low-rank representation［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):171-184.

［14］CANDES E,PLAN Y.Matrix completion with noise［J］.IEEE Proceeding,2010,98(6):925-936.

［15］路阳.多尺度改进非局部平均图像去噪算法［D］.西安:西安电子科技大学,2012.

［16］MAHMOUDI B,SAPIRO M.Fast image and video denoising via non-local means of similar neighborhoods［J］.IEEE Signal Process,2005,12(12):839-842.

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