纹理分布分析的快速图像修复算法

目前基于样本块的图像修复算法均是运用平方差和(SSD)准则遍历固定的样本集以选取最优匹配块,算法普遍具有运算效率低的缺点。针对现有算法进行改进,提出一种基于图像纹理分布分析的快速图像修复算法,该算法根据局部纹理变化动态确定样本集大小,解决样本集过大时引起的计算时间浪费以及样本集过小时样本多样性不足的问题。实验结果证明,该算法保证修复结果连续且符合人眼视觉要求,大大提高了图像修复的效率,具有实际意义。

Fast image inpainting algorithm using color texture distribution analysis

The computation cost of the current exemplar-based image inpainting algorithms is high due to the fact that the current algorithms compare the exemplar patches with the inpainting patch one after another.In this paper we propose a fast image inpainting algorithm based on color texture distribution analysis which determines the range of exemplar sets according to local texture variances,thus avoiding wasting amount of time searching and comparing with exemplar patches in vain when the examplar patches set is too large and the lack of the diversity when the examplar set is too small.The results show that the presented algorithm can ensure good performance of visually high quality while being consistent with the surrounding textures and greatly improving the computation efficiency greatly.