Volume 43 Issue 4
May  2014
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Wen Donghai, Jiang Yuesong, Hua Houqiang, Yu Rong, Zhang Yanzhong. Algorithm for speckle reduction of laser radar polarization active image[J]. Infrared and Laser Engineering, 2014, 43(4): 1130-1134.
Citation: Wen Donghai, Jiang Yuesong, Hua Houqiang, Yu Rong, Zhang Yanzhong. Algorithm for speckle reduction of laser radar polarization active image[J]. Infrared and Laser Engineering, 2014, 43(4): 1130-1134.

Algorithm for speckle reduction of laser radar polarization active image

  • Received Date: 2013-08-11
  • Rev Recd Date: 2013-09-20
  • Publish Date: 2014-04-25
  • In order to reduce speckle noise of laser active polarization image, a shock anisotropic denoising model was proposed. The model utilized Smallest Univalue Segment Assimilating Nucleus (SUSAN) algorithm to extract image edge which reduced the noise influence on edge detection influence. The approach adjusted coefficient of the shock filter automatically, which made new algorithm both retain the image edge, and restrain image speckle. The threshold of SUSAN was estimated by eight direction order difference, which maked the estimation more accuracy. Fully developed regional average absolute error was used as the standard of iterative termination conditions. Through comparing the equivalent numbers of looks (ENL) and edge preserve index (EPI), the proposed algorithm provides more effective speckle reduction as well as edge preservation.
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Algorithm for speckle reduction of laser radar polarization active image

  • 1. School of Electronic Information Engineering,Beihang University,Beijing 100191,China

Abstract: In order to reduce speckle noise of laser active polarization image, a shock anisotropic denoising model was proposed. The model utilized Smallest Univalue Segment Assimilating Nucleus (SUSAN) algorithm to extract image edge which reduced the noise influence on edge detection influence. The approach adjusted coefficient of the shock filter automatically, which made new algorithm both retain the image edge, and restrain image speckle. The threshold of SUSAN was estimated by eight direction order difference, which maked the estimation more accuracy. Fully developed regional average absolute error was used as the standard of iterative termination conditions. Through comparing the equivalent numbers of looks (ENL) and edge preserve index (EPI), the proposed algorithm provides more effective speckle reduction as well as edge preservation.

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