摘要
针对Criminisi等人提出的基于样本的图像修复算法使用穷尽搜索的方式寻找最优匹配像素块,以及采用固定大小的修复像素块进行修复时产生的错误匹配和信息延伸对图像修复质量的影响,根据像素点周围邻域信息对该像素点的决定作用和结构信息的重要性,提出一种区域搜索和自适应模板图像修复方法,以增强信息的局部协调性和边界信息的恢复能力,提高图像整体的修复效果。大量实验表明,改进算法在减少修复时间的同时,能较好地保持图像的结构,从而使修复结果达到更好的视觉效果。
The exemplar-based image inpainting algorithm proposed by Criminisi and his partner, which uses exhaustive searching to get the best-matching patch and adopts fixed-size patch to fill the image, will cause error matching and information extension. For this problem, based on the decisive role of a pixel＇ s neighboring block in the pixel＇ s value and the importance of structure information, this paper presents an image inpainting algorithm based on regional searching and adaptive template to enhance the local information harmony and boundary recovery ability, so it can improve the whole inpainting effect. A number of experiment results demonstrate that the improved method not only reduces the inpainting time and keeps the structure better, but also has a better visual effect.
出处
《计算机工程与应用》
CSCD
2013年第21期160-163,171,共5页
Computer Engineering and Applications
