Speckle reduction algorithm for laser underwater image based on curvelet transform |
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作者姓名: | 倪伟 郭宝龙 杨镠 费佩燕 |
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作者单位: | Institute of ICIE Xidian University,Xi'an 710071,Institute of ICIE,Xidian University,Xi'an 710071,Institute of ICIE,Xidian University,Xi'an 710071,Institute of ICIE,Xidian University,Xi'an 710071 |
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基金项目: | This work was supported by the National Natural Science Foundation of China under Grant No. 60572152. |
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摘 要: | Based on the analysis on the statistical model of speckle noise in laser underwater image, a novel speckle reduction algorithm using curvelet transform is proposed. Logarithmic transform is performed to transform the original multiplicative speckle noise into additive noise. An improved hard thresholding algorithm is applied in curvelet transform domain. The classical Monte-Carlo method is adopted to estimate the statistics of contourlet coefficients for speckle noise, thus determining the optimal threshold set. To further improve the visual quality of despeckling laser image, the cycle spinning technique is also utilized. Experimental results show that the proposed algorithm can achieve better performance than classical wavelet method and maintain more detail information.
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