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自适应斑点抑制方法通常基于局域变差系数实现SAR图像的异质性测量。但是这种测量方法对斑点噪声较为敏感,从而影响斑点抑制性能。本文从信息论的角度出发,提出了一种新的定量化测量SAR图像异质性参数的指标,进而提出了一种基于定量化异质性测量的斑点噪声抑制算法(HBSRF)。该算法首先以基于信息论的异质性测量为标准将SAR图像区分为同质区域和异质区域,然后利用有限迭代处理和边沿保持算法来实现斑点抑制和边沿保持性能的最优化。计算机仿真实验结果表明:该算法与经典的基于局域变差系数的斑点抑制算法相比,具有更好的抑制斑点噪声和保持边沿细节的处理性能,是一种有效的斑点抑制滤波算法。
Adaptive speckle suppression methods usually measure the heterogeneity of SAR images based on local variation coefficients. However, this measurement method is more sensitive to speckle noise, which affects the speckle suppression performance. In this paper, we propose a new quantitative index to measure the heterogeneity of SAR images from the perspective of information theory, and then propose a speckle noise suppression algorithm (HBSRF) based on quantitative heterogeneity measurement. The algorithm firstly divides the SAR images into homogeneous regions and heterogeneous regions based on the information theory-based heterogeneity measurement, and then uses the limited iterative processing and the edge preserving algorithm to optimize the speckle suppression and edge preserving performance. Computer simulation results show that this algorithm has better performance of suppressing speckle noise and preserving the details of the edge than the classical speckle reduction algorithm based on local variation coefficient, and it is an effective speckle reduction filtering algorithm.