结构测度约束下基于加权分形的图像修复算法

在获取待修复块的最佳填充块时，为了扩大搜索匹配范围，并适当提高结构像素在搜索匹配过程中的权重，提出了一种局部结构测度约束下的基于加权分形的图像修复算法．该方法对选定的定义域块进行几何变换和同构变换，构造码本，扩大搜索匹配范围; 计算各向异性非线性结构张量，得到局部结构测度，据此构造归一化权系数; 进行亮度变换，在局部结构测度的约束下，将待修复块与码本块进行加权匹配，通过最小化加权误差，导出新的亮度变换参数; 利用码本中加权误差最小的数据块来填补待修复块．实验表明:该方法能够很好地补全破损的几何结构，并使得新填充区域与源区域保持很好的一致性，其修复结果的主观质量和客观评价指标都得到了显著提高．

Image inpainting algorithm based on the weighted fractal under the structure-measurement constraint

Aiming at enlarging the searching scope and moderately raising the weights of structure pixels during searching and matching in the process of calculating the best repairing patch for each damaged patch, an image inpainting algorithm based on the weighted fractal under the structure-measurement constraint is presented. First, selected domain blocks are transformed by geometrical transform and isomorphic transform. Using the resulting blocks, the codebook is constructed and serves as the enlarged searching scope. By computing the anisotropic nonlinear structure tensor, the local structure measurement is obtained, in accordance with which the normalized weight map is constructed. The luminance transform is performed, in which we derive its new parameter formulas by minimizing the weighted error between the damaged patch and each domain block in the codebook under the structure-measurement constraint. Lastly, we search in the codebook for the best repairing patch with the minimal weighted error, and then the best repairing patch is used to fill in the damaged patch. Experimental results show that the proposed method can restore damaged structures well and simultaneously make the newly filled region maintain good consistency with the source region. The subjective quality and objective evaluation of the inpainted image are both improved greatly.