基于灰度熵合成样本块的图像修复算法

摘要

The performance of image inpainting is dependent on the utilization of known information for inpainting the missing parts of the images,and fast speed of inpainting is required.Aiming at these two key issues,an inpainting algorithm is proposed which selects samples according to the average gray entropy and obtains the final matching block through weighted synthesis.The algorithm divides the image to be repaired into grids and calculates the average local entropy in each grid.The Otsu threshold segmentation algorithm is used to classify all grid areas into two mutually exclusive sets.The proposed algorithm determines the range of the sample block according to the average gray entropy of the grid in the area to be repaired,picks up sample blocks according to the Sum of Squared Differences(SSD)criterion, uses attenuation function to determine the weight of each sample block,and ultimately synthesize the final sample block. Experimental results show that the proposed algorithm can achieve good inpainting effect and greatly improve the speed of inpainting .%图像修复时需要利用已知信息修补图像中的缺失部分，同时要求取得较快的修复速度。为此，提出基于平均灰度熵选取样本并通过加权合成最终匹配块的图像修复算法。将待修复图像进行网格划分，以网格为单位区间计算每个网格内图像的平均局部灰度熵值。使用自适应阈值分割算法将所有网格区域分为2个互斥集合。根据待修复区域所在网格平均灰度熵确定样本块的选取范围，使用最小平方差和准则选择若干样本块，并通过衰减函数得到各个样本块的权值，最终合成样本块。实验结果表明，该算法在取得理想修复效果的同时能够确保得到较快的修复速度。