利用图像结构成分的优先块匹配图像修复方法

针对基于样本块图像修复方法中样本块优先级计算易受纹理信息影响,引起修复顺序偏差,进而造成最终修复结果中结构特征不连续的问题,将图像结构成分引入修复过程,提出一种基于图像结构成分计算样本块优先级的图像修复方法.首先通过自适应局部拉普拉斯滤波器对待修复图像进行保边细节平滑处理,得到图像结构成分;然后利用结构成分和原图像共同计算样本块数据项,并以此确定样本块修复优先级,实现图像修复.通过增加结构成分引导方法,在基于等照度线和基于结构张量的图像修复算法上对常用修复测试图像进行实验,结果表明,增加结构成分引导的方法相对于原方法可改进修复效果.

Image Inpainting Using Image Structural Component and Patch Matching

Image inpainting methods base on exemplar have discontinuous structure in the repaired image,which usually caused by the repairing sequence error due to the improper calculation of patches priority.This paper proposes an image inpainting method which calculates the patches priority based on image structural component. First, the adaptive local Laplacian filters are used to edge-aware detail smoothing of the inpainting image, and acquire the structural component image. Then the data term of every border patch is calculated together with the structural component image and the corrupted image, and the priority is computed for each border patch by the data term. Finally, the corrupted image is inpainted according to the obtained priorities. The experimental results show that the inpainting methods guided by the structural component can achieve better visual effect compared to the original methods.