Image denoising using principal component analysis in the wavelet domain

被引：16

作者：

Bacchelli, S ^{[1
]}

Papi, S ^{[1
]}

机构：

[1] Univ Bologna, Dept Math, I-47023 Cesena, Italy

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2006年 / 189卷 / 1-2期

关键词：

wavelet packets; KL transform; filter banks; recursive matrices; image denoising;

D O I：

10.1016/j.cam.2005.04.030

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this work we describe a method for removing Gaussian noise from digital images, based on the combination of the wavelet packet transform and the principal component analysis. In particular, since the aim of denoising is to retain the energy of the signal while discarding the energy of the noise, our basic idea is to construct powerful tailored filters by applying the Karhunen-Loeve transform in the wavelet packet domain, thus obtaining a compaction of the signal energy into a few principal components, while the noise is spread over all the transformed coefficients. This allows us to act with a suitable shrinkage function on these new coefficients, removing the noise without blurring the edges and the important characteristics of the images. The results of a large numerical experimentation encourage us to keep going in this direction with our studies. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：606 / 621

页数：16

共 50 条

[41] A New Guided Image Denoising by Principal Component Analysis with Local Pixel Grouping
Liu, Xinji
Dai, Tao
[J]. 2018 IEEE FOURTH INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM), 2018,
[42] SAR Image Denoising via Clustering-Based Principal Component Analysis
Xu, Linlin
Li, Jonathan
Shu, Yuanming
Peng, Junhuan
[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (11): : 6858 - 6869
[43] Displacement field denoising for high-temperature digital image correlation using principal component analysis
Hao, Wenfeng
Zhu, Jianguo
Zhu, Qi
Chen, Lei
Li, Longkang
[J]. MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, 2017, 24 (10) : 830 - 839
[44] Image Denoising Using Quadtree-Based Nonlocal Means With Locally Adaptive Principal Component Analysis
Zuo, Chenglin
Jovanov, Ljubomir
Goossens, Bart
Hiep Quang Luong
Philips, Wilfried
Liu, Yu
Zhang, Maojun
[J]. IEEE SIGNAL PROCESSING LETTERS, 2016, 23 (04) : 434 - 438
[45] Image sharpening via image denoising in the complex wavelet domain
Shi, F
Selesnick, IW
Cai, SH
[J]. WAVELETS: APPLICATIONS IN SIGNAL AND IMAGE PROCESSING X, PTS 1 AND 2, 2003, 5207 : 467 - 474
[46] Image Denoising by Principal Basis Analysis
Sun, Hong
Sang, Cheng-Wei
Liu, Chen-Guang
[J]. MIPPR 2015: MULTISPECTRAL IMAGE ACQUISITION, PROCESSING, AND ANALYSIS, 2015, 9811
[47] An Adaptive Image Denoising Algorithm Based On Wavelet Transform And Independent Component Analysis
Liu Zongang
Wang Tong
[J]. PROCEEDINGS 2015 SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND ENGINEERING APPLICATIONS ISDEA 2015, 2015, : 104 - 107
[48] Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain
Fan, Wen-quan
Xiao, Wen-shu
Xiao, Wen-shu
[J]. JOURNAL OF ENGINEERING-JOE, 2019, 2019 (19): : 6012 - 6015
[49] A spike denoising method combined principal component analysis with wavelet and ensemble empirical mode decomposition
Zhou Y.
Hu Y.
Li M.
Yang L.
Shang Z.
[J]. Shang, Zhigang (zhigang_shang@zzu.edu.cn); Shang, Zhigang (zhigang_shang@zzu.edu.cn), 1600, West China Hospital, Sichuan Institute of Biomedical Engineering (37): : 271 - 279
[50] Images' Principal Component Analysis using Hyperanalytic Wavelet Packets
Magu, Georgiana-Alina
Kovaci, Maria
[J]. 2018 13TH INTERNATIONAL SYMPOSIUM ON ELECTRONICS AND TELECOMMUNICATIONS (ISETC), 2018, : 324 - 327

← 1 2 3 4 5 →