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A new efficient variational model for multiplicative noise removal

机译:一种新的高效变分模型,用于乘法噪声拆除

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摘要

In this paper, based on the approximation of the Taylor expansion, a novel fidelity term is formulated. We propose a new variational image restoration model to recover an image from its multiplicative noised version. We show that the proposed model is strictly convex and has a unique solution so that the model can be performed by the primal-dual method. Compared with other models, the proposed model is able to not only deal with various problems of different type noises, such as the multiplicative Beta noise, F noise, Gaussian noise, but also take significantly less computational time.
机译:本文基于泰勒膨胀的近似,制定了一种新的富达项。我们提出了一种新的变分型图像恢复模型,以从其乘法声明版本恢复图像。我们表明,所提出的模型是严格凸的,具有独特的解决方案,使得模型可以通过原始方法执行。与其他模型相比,所提出的模型不仅能够处理不同类型噪声的各种问题,例如乘法β噪声,F噪声,高斯噪声,还考虑了显着较少的计算时间。

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