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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. 相似文献
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一种新的基于非下采样Contourlet变换的自适应图像去噪算法 总被引:2,自引:0,他引:2
提出了一种新的结合非下采样Contourlet变换(NSCT)和斯坦无偏风险估计(SURE)的自适应图像去噪方法.通过NSCT对含噪图像进行分解,根据斯坦无偏风险估计准则对分解后的噪声图像进行均方误差E估计,并依据得剑的E<,MS>构造线性自适应阚值方程,对含噪图像的每一个分解子带进行阈值去噪.对自适应阈值去噪后的图像分解子带进行重构.得到去噪图像.实验结果表明,该方法可以有效地消除标准图像和自然图像中的噪声,在去噪图像峰值信噪比(PSNR)和边缘保持性能上都优于已有算法. 相似文献
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Two image denoising approaches based on wavelet neural network (WNN) optimized by particle swarm optimization (PSO) are proposed. The noisy image is filtered by the modified median filtering (MMF). Feature values are extracted based on the MMF and then normalized in order to avoid data scattering. In approach 1, WNN is used to tell those uncorrupted but filtered by MMF and then the pixels are restored to their original values while other pixels will retain. In approach 2, WNN distinguishes the corrupted pixels and then these pixels are replaced by MMF results while other pixels retain. WNN can be seen as a classifier to distinguish the corrupted or uncorrupted pixels from others in both approaches. PSO is adopted to optimize and train the WNN for its low requirements and easy employment. Experiments have shown that in terms of peak signal-to-noise ratio (PSNR) and subjective image quality, both proposed approaches are superior to traditional median filtering. 相似文献
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基于非采样Contourlet变换的遥感图像融合算法 总被引:9,自引:5,他引:4
为了使融合后的多光谱图像在尽可能保持原始多光谱图像光谱特性的同时,显著提高空间分辨力,提出了一种基于非采样Contourlet变换(NSCT)的遥感图像融合算法。算法首先对全色波段图像进行非采样Contourlet变换,得到全色波段图像的低频子带系数和各带通方向子带系数;然后针对多光谱图像的每一个波段,将其进行双线性插值后作为融合后多光谱图像的低频子带系数,对全色波段图像的各带通方向子带系数采用基于成像系统物理特性的注入模型(调整系数)进行局部调整后,作为融合后多光谱图像的各带通方向子带系数,从而得到融合后多光谱图像的非采样Contourlet变换系数;最后再经非采样Contourlet逆变换得到该波段具有高空间分辨力的多光谱图像。采用IKONOS卫星遥感图像进行了仿真实验,实验结果表明,该算法在光谱保留和空间质量提高方面优于其它传统的遥感图像融合算法。 相似文献
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A fast video stabilization method is presented, which consists of sub-image phase correlation based global motion estimation, Kalman filtering based motion smoothing and motion modification based compensation. Global motion is decided using phase correlation in four sub-images. Then, the motion vectors are accumulated to be Kalman filtered for smoothing. The ordinal motion compensation is applied to each frame with modification to prevent error propagation. Experimental results show that this stabilization system can remove unwanted translational jitter of video sequences and follow intentional scan at real-time speed. 相似文献