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Reweighted l2 norm minimization approach to image inpainting based on rank minimization

机译:基于秩最小化的加权l 2 范数最小化方法

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This paper proposes a rank minimization based approach to a novel image painting. We utilize the 2-D autoregressive (AR) model to describe the image data, and formulate the image inpainting problem as the system identification problem of finding the minimum order system. This problem is described as the rank minimization problem, which is NP hard in general. To solve the problem approximately, this paper proposes a fast algorithm based on the iterative reweighted least square (IRLS). Numerical examples show that the proposed algorithm recovers missing pixels well.
机译:本文提出了一种基于等级最小化的新颖图像绘画方法。我们利用二维自回归(AR)模型来描述图像数据,并将图像修复问题表达为寻找最小阶系统的系统识别问题。这个问题被描述为秩最小化问题,通常是NP难的。为了大致解决该问题,本文提出了一种基于迭代最小加权平方(IRLS)的快速算法。数值算例表明,该算法可以很好地恢复像素丢失。

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