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A new approach of image inpainting based on PSO algorithm

机译:一种基于PSO算法的图像染色方法

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In this paper, an efficient approach is developed to incorporate the exemplar-based image of inpainting method and the minimum error boundary of cut technique, that is proposed to improve the image inpainting in a more nature quality with high performance. The approach is based on the particle swarm optimization. Several advantages are addressed as follow. First, the image inpainting in texture with the linear structure is exploited to reasonably inpaint the damaged image in a high priority. Due to set the first priority to inpaint in the linear structure, such a method guarantees the integrality of the linear structure. In addition, the minimum error boundary of cut technique can effectively decrease the unnatural phenomena through inpainting the gap of damaged image. Owing to the damaged one will be dawdling extraordinarily if searching the similar block while inpainting. In this case, the method is proposed by joined the particle swarm optimization algorithm, and the outcome indeed improves the efficiency of image inpainting.
机译:在本文中,开发了一种有效的方法来纳入基于示例性的初始化方法和切割技术的最小误差边界,这提出了提高具有高性能的更高性质的图像染色。该方法基于粒子群优化。几个优点如下。首先,利用线性结构的纹理中的图像染色以在高优先级地合理地侵入损坏的图像。由于将第一优先级设置为线性结构中的第一优先级,这种方法保证了线性结构的整体。另外,通过侵蚀损坏图像的间隙,切割技术的最小误差边界可以有效地降低不自然的现象。由于损坏的损坏,如果在染色的同时在类似的块中搜索类似的块,就会出现扭动。在这种情况下,通过加入粒子群优化算法提出了该方法,结果确实提高了图像染色的效率。

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