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Fast convergent image inpainting method based on BSCB model

机译:基于BSCB模型的快速收敛图像修复方法

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摘要

Digital image inpainting technique has been widely used in many socio-economic areas and methods based on partial differential equations (PDE) attract intensive research in the hope of an automatic inpainting methodology. However the methods based on PDE are very time-consuming because they need to be solved by numerical integration along the temporal axis. In this paper, a fast convergent image inpainting method is proposed by combining Richardson extrapolation with an improved BSCB inpainting model, which improves the convergence rate of the numerical iterations. Experimental results show that the new model is effective and efficient.
机译:数字图像修复技术已广泛应用于许多社会经济领域,基于偏微分方程(PDE)的方法吸引了广泛的研究,希望能有一种自动修复方法。但是,基于PDE的方法非常耗时,因为它们需要通过沿时间轴的数值积分来解决。本文提出了一种快速收敛的图像修复方法,该方法将Richardson外推法与改进的BSCB修复模型相结合,提高了数值迭代的收敛速度。实验结果表明,该模型是有效的。

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