Speckle reduction algorithm for laser underwater image based on curvelet transform

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.     

水下激光目标的统计对消分割法

水下激光目标的识别是一个崭新的研究领域,有许多问题需要解决,其中,目标分割是关键.水下激光图像中夹杂着严重的散斑噪声,受其影响,要识别水下激光目标,就要对图像进行有效的消噪,然后进行目标分割.本文依据具有相似统计特征的噪声可抵消图像中的相应噪声这一基理,结合小波变换和统计法,提出了一种水下激光目标的统计对消分割法,以去除噪声,提取目标.实验结果表明该方法是有效可行的.