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Ant Colony Optimization Based Anisotropic Diffusion Approach for Despeckling of SAR Images

机译:基于蚁群优化的各向异性扩散方法对SAR图像去斑

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Synthetic Aperture Radar (SAR) images are known to be corrupted by granular noise known as speckle. This noise is inherently present in these images owing to acquisition constraints and is a major cause of visual quality degradation. The anisotropic diffusion approaches for despeckling are constrained in terms exercising control over the non-homogeneous regions. This paper proposes to improve the non-linear Anisotropic Diffusion (AD) filter for despeckling using Ant Colony Optimization (ACO) algorithm. The main essence of this work is to suppress speckle and preserve the structural content. The issue of residual speckle content has been minimized by optimal selection of AD parameter(s) using ACO algorithm. Experimental results advocate the performance improvement achieved and has been validated using objective measures of image quality evaluation.
机译:已知合成孔径雷达(SAR)图像会被称为斑点的粒状噪声破坏。由于采集限制,这些图像固有地存在于这些图像中,并且是视觉质量下降的主要原因。用于散斑的各向异性扩散方法在对非均匀区域进行控制方面受到限制。本文提出使用蚁群优化(ACO)算法改进去斑的非线性各向异性扩散(AD)滤波器。这项工作的主要实质是抑制斑点并保留结构含量。残余斑点含量的问题已通过使用ACO算法优化选择AD参数而得以最小化。实验结果证明了所取得的性能改进,并已通过使用客观的图像质量评估手段得到了验证。

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