针对Criminisi算法在图像修复过程中易将纹理部分误认为边缘部分,造成修复顺序偏差而影响修复效果的不足,充分利用图像的纹理结构特征和边缘结构特征,引入差别因子,改进优先权模型,以增强对结构部分的辨别能力,并通过采用新型的搜索方式来克服在修复过程中易产生瑕疵点的不足来完善修复效果。实验结果表明,改进算法较好地克服了原算法所存在的纹理延伸等不足,保持了修复内容的线性结构,其峰值信噪比相比于原算法提高了23 dB,具有更好的视觉效果。

In the image inpainting process, the Criminisi algorithm easily confuses texture parts for edge parts, resulting in inpainting sequence deviation which finally influences the inpaint effect. By making full use of the characteristics of image textures and edges, we introduce a differential-factor to improve the ability of the algorithm to distinguish edge parts. At the same time, the improved algorithm uses a new search method to prevent the appearance of flaws during the image inpainting process. Experiments confirm that the improved algorithm can overcome the insufficiencies of the original algorithm such as texture extension and can better maintain linear structures. The Power Signal-to-Noise Ratio of the result compared to the original algorithm has been improved about 2 dB to 3 dB, and the results have a better visual appearance.