摘要:
提出了复Contourlet域(CCT)中有向图与高斯混合模型的声呐图像增强算法。采用复Contourlet分析提取各尺度中声呐图像每一方向的弱特征信息;为建立特征信息间的联系,考虑复Contourlet域相邻尺度间子带系数的状态具有Markov性,子节点系数的状态依赖于父节点系数状态,构建有向概率图模型反映复系数的这种持续性;尺度内,构建高斯混合模型来建立同尺度中特性信息的联系,以两状态高斯混合模型来表征子带系数的非高斯边缘分布;最后,采用期望最大(EM)算法训练模型参数估计增强图像的系数,实现声呐图像增强。实验结果表明,本文算法与小波域隐马尔可夫树(HMT)算法、Contourlet域HMT算法相比,峰值信噪比(PSNR)增大4 dB以上,结构相似(SSIM)指数增加0.3;本文算法不仅能较好地抑制了声呐图像的强噪声,同时保留了图像边缘和轮廓等弱特征信息。
Abstract:
A sonar image enhancement algorithm based on the directed probability graph of Complex Contourlet Transform(CCT) and Gaussian mixture model is proposed.Using complex Contourlet analysis to extract the weak feature information of each direction of the sonar image in each scale;In order to establish the relationship between the feature information,We consider that the state of subband coefficients between adjacent scales of the complex Contourlet domain has Markov property,and the state of sub-node coefficients depends on the state of parent node coefficients,and constructs a directed probability graph model to reflect this continuity of complex coefficients;Within the scale,we build a Gaussian mixture model to establish the connection of characteristic information in the same scale,and use a two-state Gaussian mixture model to characterize the non-Gaussian edge distribution of subband coefficients.Finally,the Expectation Maximization(EM) algorithm is used to train the model parameters,estimate the coefficients of the enhanced image so that it can realize the sonar image enhancement.The experimental results show that compared with the wavelet domain Hidden Markov Tree(HMT) algorithm and the Contour let domain HMT algorithm,the Peak Signal-to-Noise Ratio(PSNR) of the proposed algorithm increases by more than 4 dB,and the Structural SIMilarity(SSIM) index increases by 0.3;The algorithm in this paper can not only suppress the strong noise of the sonar image,but also retain the weak feature information such as the edge and texture of the image.
百度学术
下载:
