Volume 5 Issue 3
Jun.  2012
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LIU Xi-jia, CHEN Yu, WANG Wen-sheng, LIU Zhu. De-noising algorithm of wavelet threshold for small target detection[J]. Chinese Optics, 2012, 5(3): 248-256. doi: 10.3788/CO.20120503.0248
Citation: LIU Xi-jia, CHEN Yu, WANG Wen-sheng, LIU Zhu. De-noising algorithm of wavelet threshold for small target detection[J]. Chinese Optics, 2012, 5(3): 248-256. doi: 10.3788/CO.20120503.0248

De-noising algorithm of wavelet threshold for small target detection

doi: 10.3788/CO.20120503.0248
  • Received Date: 21 Jan 2012
  • Rev Recd Date: 13 Mar 2012
  • Publish Date: 10 Jun 2012
  • In order to obtain better de-noising effects and higher Signal-to-noise Ratios(SNRs) for recognizing small targets, the local variance estimation method is adopted to calculate the threshold. Different thresholds are selected for all the high-frequency sub-bands in wavelet decomposition levels. The improved hyperbolic function is used as the threshold function for wavelet coefficients more than the thresholds and exponential and logarithmic functions are combined as the threshold function for wavelet coefficients less than the thresholds. The adopted threshold function is derived theoretically and compared experimentally with those of soft and hard threshold methods. Computer simulation results show that the SNR is improved by 70.8% with the threshold method adopted in this paper, while they are improved by 49.8% and 59.7% respectively by using soft and hard threshold methods. By optical experiments, it is further proved that the method can improve SNRs and enhance the recognition ability of small targets with Joint Transform Correlator(JTC) more effectively.


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