Two improved methods on wavelet image denoising

被引：3
|
作者：
Lin, ZX [1 ]
Song, GX [1 ]
Xue, W [1 ]
机构：
[1] Xidian Univ, Xian 710071, Peoples R China
来源：
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词：
image denoising; threshold; spline; wavelet domain; modulus maximum;
D O I：
10.1109/ICMLC.2003.1260085
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Wavelet image denoising has been well acknowledged as an important method of denoising in Image Processing. In this paper, the principle of several wavelet denoising methods are described including the Mallat forced denoising, the wavelet transform modulus maximum method, the nonlinear wavelet threshold denoising method and the denoising based on the interrelation of wavelet domain. Also we compare with these methods and give two kinds of improved methods. The results show that it is practicable and effective for image denoising.
引用
收藏
页码：2979 / 2983
页数：5
相关论文
共 50 条
  • [21] An Improved Denoising Method of Structured Light Image Based on Wavelet Transform
    Liu, Gan
    Shao, Xinjie
    PROCEEDINGS OF THE ADVANCES IN MATERIALS, MACHINERY, ELECTRICAL ENGINEERING (AMMEE 2017), 2017, 114 : 278 - 282
  • [22] Image Denoising Based on an Improved Wavelet Threshold and Total Variation Model
    Wang, Zhi
    Ma, Fengying
    Ji, Peng
    Fu, Chengcai
    ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT VIII, ICIC 2024, 2024, 14869 : 142 - 154
  • [23] Battle-Lemarie Wavelet Pyramid for Improved GSM Image Denoising
    Mudugamuwa, Damith J.
    He, Xiangjian
    Jia, Wenjing
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 3156 - 3159
  • [24] An improved model for wavelet coefficients estimation and its application to image denoising
    Li, Xueqing
    Deng, Caixia
    Li, Shasha
    Pi, Lu
    INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2024, 22 (04)
  • [25] Image denoising method integrating ridgelet transform and improved wavelet threshold
    Li, Bingbing
    Cong, Yao
    Mo, Hongwei
    PLOS ONE, 2024, 19 (09):
  • [26] A Comparative Study on Thresholding Methods in Wavelet-based Image Denoising
    Xiao, Fei
    Zhang, Yungang
    CEIS 2011, 2011, 15
  • [27] WAVELET TRANSFORM BASED IMAGE DENOISING USING DIFFERENT THRESHOLDING METHODS
    Khan, Asim Ali
    Singh, Manoj Kumar
    FOURTH INTERNATIONAL CONFERENCE ON COMPUTER AND ELECTRICAL ENGINEERING (ICCEE 2011), 2011, : 93 - 98
  • [28] Parsimony and wavelet methods for denoising
    Krim, H
    Pesquet, JC
    Schick, IC
    PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6, 1998, : 1869 - 1872
  • [29] Improved Deadzone Modeling for Bivariate Wavelet Shrinkage-Based Image Denoising
    DelMarco, Stephen
    MOBILE MULTIMEDIA/IMAGE PROCESSING, SECURITY, AND APPLICATIONS 2016, 2016, 9869
  • [30] Image denoising: An approach based on wavelet neural network and improved median filtering
    Yan, Yunyi
    Guo, Baolong
    Ni, Wei
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 628 - 628
12345