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A non-local means based vectorial total variational model for multichannel SAR image denoising

机译:基于非局部均值的矢量总变分模型的多通道SAR图像去噪

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A non-local vectorial total variational model is proposed for multichannel Synthetic Aperture Radar (SAR) image denoising. By introducing non-local means to the vectorial total variational model, it is found that the new non-local vectorial total variational model is well performed on denoising while preserving the fine structures of the multichannel SAR images. The energy functional of non-local gradient formed vectorial total variation is well constructed. Followed, the discrete version of this model is designed for constructing the fixed point iteration to solve the proposed model. The new algorithm is implemented on multipolarization RADARSAT-2 images. Result shows that the non-local vectorial total variational model fits well. The convergence is proved as well.
机译:针对多通道合成孔径雷达(SAR)图像去噪,提出了一种非局部矢量总变分模型。通过将非局部均值引入矢量总变分模型,发现新的非局部矢量总变分模型在去噪方面表现良好,同时保留了多通道SAR图像的精细结构。很好地构造了非局部梯度形成的矢量总变化量的能量函数。随后,该模型的离散版本被设计用于构造定点迭代以解决所提出的模型。该新算法在多极化RADARSAT-2图像上实现。结果表明,非局部矢量总变分模型拟合良好。收敛也被证明。

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