基于奇異性檢測(cè)的信號(hào)去噪新方法
Denoising by Singularity Detection
-
摘要: 該文引進(jìn)了一種基于奇異性檢測(cè)的信號(hào)去噪方法,并對(duì)其在二維降噪中所需進(jìn)行的復(fù)雜的線性內(nèi)插作了進(jìn)一步簡(jiǎn)化,使得整個(gè)二維降噪得以大大簡(jiǎn)化而達(dá)到快速運(yùn)算和節(jié)省存儲(chǔ)量的目的。文中詳細(xì)描述了該算法的理論基礎(chǔ)并給出其一維計(jì)算機(jī)仿真,同時(shí)也給出了進(jìn)一步簡(jiǎn)化后的二維降噪仿真。這種去噪方法不需要信號(hào)或噪聲的先驗(yàn)信息。仿真結(jié)果表明,相比其它小波去噪方法,該方法的主要優(yōu)勢(shì)在于:它在某一時(shí)刻的脈沖噪聲的辨識(shí)和去除能力相當(dāng)強(qiáng),而且在去噪的同時(shí)能很好地保持信號(hào)邊緣。
-
關(guān)鍵詞:
- 噪聲; 李氏指數(shù); 奇異性; 小波
Abstract: A signal denoising algorithm based on singularity detection is introduced in this paper, It simplified the complicated linear interpolation operation needed in the 2-D image denoising so that the 2-D denoising is greatly simplified and it can also get the fast denoising and save lots of memory. A complete description of this method and its 1-D denoising simulation are presented. A simplified 2-D denoising simulation is presented, too. This method does not need the prior information of signal or noise. Simulation results indicate that compared to other wavelet based denoising algorithms, the main advantage of this method is: it can better detect and reduce the pulse noise and it can reduce the noise while keeping the signal edges better. -
謝杰成,張大力,徐文立.小波圖象去噪綜述.中國圖象圖形學(xué)報(bào),2002,7(3):209-217.[2]Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage[J].Biometrika.1994, 81(3):425-[3]Mallat S, Hwang W L. Singularity detection and processing with wavelets, IEEE Trans[J].on Information Theory.1992, 38(3):617-[4]Hsung Tai-Chiu, Lun Daniel Pak-Kong, Siu Wan-Chi. Denoising by singularity detection[J].IEEE Trans. on Signal Processing.1999,47(11):3139-[5]彭玉華.小波變換與工程應(yīng)用.北京:科學(xué)出版社,1999:38-62.[6]徐長(zhǎng)發(fā),李國寬.實(shí)用小波方法.武漢:華中科技大學(xué)出版社,2001:210-225,235-246. -
計(jì)量
- 文章訪問數(shù): 2162
- HTML全文瀏覽量: 110
- PDF下載量: 634
- 被引次數(shù): 0
下載:
