基于分组行进算法的图像修补方法

图像修补是图像恢复研究中的一个重要内容,它的目的是根据图像的现有信息来自动恢复丢失的信息。虽然图像修补的基本思想十分简单,但是许多的图像修补算法都十分复杂,而且难于实现。快速行进算法(FMM)与水平集法(Level Set)相结合进行曲线进化是一种高效的曲线进化算法,该算法的时间复杂度是O(NlbN)。Kim提出了另一种水平集的曲线进化算法——分组行进算法(GMM),该算法的时间复杂度是O(N)。受其启发,为了更快地进行图像修补,提出了一种基于GMM算法的图像修补的新算法,并研究了对GMM算法的细节改进。为了验证算法的快速性,还给出了使用Bertalmio提出的算法、Telea提出的算法以及新算法对同一幅图片进行修补的实验结果。通过比较发现,该新算法在大幅度提高修补速度的同时,仍能保持较好的修补效果。

Digital Image Inpainting Based on Group Marching Method

Image inpainting is an important research topic in the area of image restoration.Its objective is to restore the lost information according to around image information.Although the inpainting basics are straightforward,most inpainting techniques published in the literature are complex to understand and implement.Fast marching method(FMM) is an efficient algorithm for level set applications whose total computation cost is O(NlbN)).Kim presented a more efficient algorithm called group marching method(GMM) with the complexity of O(N).Motivated by his work,we propose a new technique for image inpainting based on GMM.Examples of experiment using Bertalmio's algorithm,Telea's algorithm,and our algorithm are illustrated.The result shows that the technique we proposed is faster than the other inpainting methods while preserving almost the same inpainting result.