大多数基于样本块的图像修复算法通过平移来寻找最优匹配块,当破损区域与信息区域存在旋转或尺度缩放时,这些算法则无法对图像进行有效修复。针对这一问题,提出一种基于样本块的旋转及缩放图像修复算法。利用局部特征向量获得与破损区域具有旋转或尺度缩放关系的最优匹配块;对匹配块的空间能量函数进行拓展;实现具有旋转及尺度缩放破损图像的自动修复。实验结果表明,对于具有旋转和尺度缩放的图像,本文算法能够准确搜寻到破损块的最优匹配块,提高了匹配准确率,并且修复效果较好,修复效率较高,稳健性较强。

Most exemplar-based methods find the optimal matching block by translation, which fail to deal with the images when there is rotation or scaling transformation between source and damaged region. We propose an exemplar-based inpainting algorithm with rotation and scaling transformation. Firstly, the optimal patches, with rotation or scaling transformation in those damaged region, are obtained based on the local feature vectors. Then, the energy space function is expanded to adapt the transformation of patch in data filling. Finally, the damaged image with rotation or scaling transformation can be inpainted automatically. The experimental results show that the proposed method can accurately find the optimal matching block when rotation or scaling transformation exists, and achieve better effect, higher effectiveness and robustness.