Information Technology Journal1812-56381812-5646Asian Network for Scientific Information10.3923/itj.2014.118.125ZhaoJuanjuan JiGuohua WeiWei WangJin LiuYongxing WangQuan QiangYan 12014131This study presents a new wavelet-based noise reduction scheme
based on the lifting scheme and genetic algorithms, which is a novel approach
by using a Genetic Algorithm and lifting wavelet framework for threshold selection.
There are two folders in this approach. Firstly, it adapts itself to various
types of noises without any prior knowledge of noise; secondly, it suppresses
noises while preserving the dynamics of the signals. The experimental evaluation
is conducted to compare the performances of the new method with existing approaches
and the applications for signal denoising are investigated in this study.]]>Wei, G.L. and H.S. Shu,200733663670Han, M. and Y. Liu,2009361006010067Liu, Y. and X. Liao,20113813461355Sun, J., Y. Zhao, J. Zhang, X. Luo and M. Small,20072007Marcelin, J.L.,200117910915Sweldens, W.,19951995pp: 6879Holland, J.H.,19751st Edn.,Pages: 183Pages: 183Chakraborty, I., V. Kumar, S.B. Nair and R. Tiwari,200335649659Shao, M. and K.E. Barner,20061519001915Han, M., Y. Liu, J. Xi and W. Guo,2007146265Wei, W. and Y. Qi,20111147944807Wei, W. and L. Jun,20126364372Wei, W. and H. Ma,2012121-12617991803Wei, W., A. Gao, B. Zhou and Y. Mei,2010911961201Wei, W., B. Zhou, A. Gao and Y. Mei,2010914151420