利用几何结构检测去除图像中的随机值脉冲噪声

尽管中值滤波以及各种改进方法是去除图像中随机值脉冲噪声的有效方法,然而,大多数去噪方法存在门限值选取困难和对图像边缘纹理结构过平滑的缺点。针对这一问题,提出了一种基于几何结构的用于检测和去除随机值脉冲噪声的新方法。该方法首先利用图像的直方图分布来估计脉冲噪声的噪声率;然后进一步基于噪声率和细节图像的直方图分布,自适应地确定两个分类门限;最后利用两个门限,将细节图像中的像素分成‘未被污染点’、‘待定点’和‘噪声点’。其中‘待定点’主要由边缘和纹理区像素和噪声像素构成,为区分其属性,还引入了几何结构检测方法。基于各像素点的类型,细节图像被用于修正中值滤波的结果。实验结果表明,该新方法在去除脉冲噪声的同时,还很好地保留了图像的边缘结构。与已有的方法相比,具有明显的优势。

一种改进的非下采样轮廓波变换图像去噪算法

优化图像去噪问题,在非下采样轮廓波变换图像去噪中,收缩阈值的确定仅依赖变换子带系数的幅值,使得过多图像系数和噪声系数一并去除,导致滤波图像模糊。从检测变换子带几何结构出发,引入自蛇模型对子带系数作几何结构检测并抑制噪声后,估计双阈值将子带系数划分为三类并作不同处理,实现对噪声系数的去除和对图像系数的保护。实验结果表明,相对现有典型算法,改进算法获得的峰值信噪比提高了0.1-0.9dB,图像系数被更好识别和保留,滤波图像中边缘与区域细节损失减少,提高去噪效果,保留图像的有效信息。

Morphological Research on Geometrical Scattering Waves of an Underwater Target

In this paper, a new method based on morphologic research named reconstruction cross-component removal （RCCR） is developed to analyze geometrical scattering waves of an underwater target. Combining the origin of the cross-component in Wigner-ViUe distribution, the highlight model of target echoes and time-frequency features of linear frequency-modulated signal can remove cross-components produced by multiple component signals in Wigner-Ville distribution and recover the auto-components of output signals. This method is used in experimental data processing, which can strengthen the real geometric highlights, and restrain the cross components. It is demonstrated that this method is helpful to analyze the geometrical scattering waves, providing an effective solution to underwater target detection and recognition.