Adaptive Denoising Algorithms Based on Wavelet for Pool Underwater Image

Yi Wang; Fei Lei; Guang Jie Fu

doi:10.4028/www.scientific.net/AMM.333-335.1024

Paper Titles

Digital Image Processing and Coordinate Transformation for Vic-3D System
p.1002

Imaging Analysis and Application of Digital Speckle Photography with EALCD
p.1007

Solid-Liquid Two-Phase Flow Image Reconstruction in Microchannel Based on Tikhonov Regularization Method
p.1013

Moving Objects Detection Based on Gaussian Models with HSV
p.1020

Adaptive Denoising Algorithms Based on Wavelet for Pool Underwater Image
p.1024

Object Tracking Using Improved Meanshift Algorithm Combined with Kalman Filter on Independent Visual Robotic Fish
p.1030

Photographic Volume Visualization with LH Histogram and Color Distance Gradient
p.1034

Wafer Image Registration Based on Hough Transform
p.1038

Moving Object Detection for Video Based on Image Blocking and Improved Particle Filter
p.1043

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 333-335Adaptive Denoising Algorithms Based on Wavelet for...

Adaptive Denoising Algorithms Based on Wavelet for Pool Underwater Image

Abstract:

Image denoising is an important step in image processing of pools intelligent life-saving system, adaptive denoising algorithms has based on wavelet in this paper. Firstly, the threshold value selection was discussed and then the effect of threshold functions on denoising result was evaluated. Finally, an improved adaptive nonlinear threshold function was put forward. Simulation results showing that the adaptive denoising algorithm designed in this paper can achieve higher PSNR value, and time cost can be satisfy the real-time demand of pool intelligent life-saving system simultaneously.

You might also be interested in these eBooks

Measurement Technology and Engineering Researches in Industry

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 333-335)

Pages:

1024-1029

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.333-335.1024

Citation:

Cite this paper

Online since:

July 2013

Authors:

Yi Wang*, Fei Lei, Guang Jie Fu

Keywords:

Adaptive Denoising, Nonlinear Threshold Function, Pool, Underwater Image, Wavelet

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] Mallat S. A Wavelet Tour of Signal Processing [M]. BeiJing. China Machine Press(2003)pp: 10-23.

Google Scholar

[2] Ping GAO, Jing ZU, Wavelet Denoising Technology Tnalysis Based on MATLAB [J]. Information Technology(2007) 6, pp: 1-3（In Chinese）.

Google Scholar

[3] Wei LIU, Research of Underwater Image Processing algorithm based on edge"[D], Ha'erbin, Ha, erbin Engineering University, (2003). （In Chinese）.

Google Scholar

[4] Rafael C. Gonzalez, and Richard E. Woods. Digital image processing[M],. BeiJing Electornic Industries Publishing Company, December(2007).

Google Scholar

[5] Tian-sheng GUO, Image Denoising Based on Wavelet Algorithms. LanZhou, LanZhou University of Technology(2010) （In Chinese）.

Google Scholar

[6] S. Zhang, E. Salari. Image denoising using a neural network based non-filter in wavelet domain [J]. March (2005), Richmond, KY, USA pp: 18-23.

Google Scholar

[7] Jie LEI, Xuan-hao DING. Improved Wavelet Adaptive Threshold Image Denoising[J], Journal of GuiLin University of Electronic Technology, (2006). 26(5), pp: 351-354（In Chinese）.

Google Scholar

[8] Prabhakar C.J. Praveen Kumar P.U. Underwater Image Denoising Using Adaptive Wavelet Subband Thresholding,. International Conference on Signal and Image Processing [J] (2010), New Delhi, India. pp: 322-327.

DOI: 10.1109/icsip.2010.5697491

Google Scholar

[9] Xingmei Li, Guoping Yan, and Liang Chen. Image denoise based on soft threshold and edge enhancement [J], Electronic Measurement Technology. May 3(2007) Wuhan, China. pp.10-12（In Chinese）.

Google Scholar

[10] Junmei Zhong. Image denoising based on wavelets and multifractals for singularity detection [M]. Dct (2005) NY.

DOI: 10.1109/tip.2005.849313

Google Scholar