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A new total variation model for restoring blurred and speckle noisy images

机译:一种新的全变异模型,用于恢复模糊和散斑噪声图像

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摘要

In coherent imaging systems, such as the synthetic aperture radar (SAR), the observed images are affected by multiplicative speckle noise. This paper proposes a new variational model based on I-divergence for restoring blurred images with speckle noise. The model minimizes the sum of an I-divergence data fidelity term, a new quadratic penalty term based on the statistical property of the noise and the total-variation regularization term. The existence and uniqueness of a solution of the proposed model with some other characteristics are analyzed. Furthermore, an iterative algorithm is introduced to solve the proposed variational model. Our numerical experiments indicate that the proposed method performs favorably.
机译:在相干成像系统中,如合成孔径雷达(SAR),观测到的图像会受到乘法散斑噪声的影响。该文提出了一种基于I发散的变分模型,用于恢复散斑噪声的模糊图像。该模型最小化了 I 发散数据保真度项的总和,这是一个基于噪声统计属性的新二次惩罚项和总变异正则化项。分析了所提模型解的存在性和唯一性。此外,还引入了一种迭代算法来求解所提出的变分模型。数值实验表明,所提方法具有较好的效果。

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