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局部特征信息约束的改进Criminisi算法
引用本文:张申华,王克刚,祝轩.局部特征信息约束的改进Criminisi算法[J].计算机工程与应用,2014(8):127-130.
作者姓名:张申华  王克刚  祝轩
作者单位:安康学院电子信息工程系;西北大学信息科学与技术学院
基金项目:陕西省教育厅专项科研计划项目(No.2013JK1199);安康学院高层次人才项目(No.AYQDZR201201)
摘    要:针对Criminisi算法计算目标块填充优先权等级时存在缺陷的问题,提出了一种改进的修复算法,方法在确立新的优先等级函数时,充分考虑图像的局部特征信息——曲率和梯度,将曲率及梯度信息作为优先权值的数据项,从而获得更加可靠的填充修复顺序。实验结果表明,和Criminisi算法相比,该方法克服了修复过程中高纹理区域向低纹理区域过度扩散的问题,并取得了更加理想的视觉修复效果。

关 键 词:图像修复  优先权  局部特征  曲率  梯度

Improved Criminisi algorithm constrained by local feature
ZHANG Shenhua;WANG Kegang;ZHU Xuan.Improved Criminisi algorithm constrained by local feature[J].Computer Engineering and Applications,2014(8):127-130.
Authors:ZHANG Shenhua;WANG Kegang;ZHU Xuan
Affiliation:ZHANG Shenhua;WANG Kegang;ZHU Xuan;Department of Electronic and Information,Ankang University;School of Information and Technology,Northwest University;
Abstract:When computing the order of target patch priority, Criminisi algorithm has defect, to solve this problem, an improved inpainting algorithm is proposed. To establish a new function about priority, the proposed algorithm takes into account local feature of image-curvature and gradient, and uses the information of curvature and gradient as a data item. Thus, the order of filling inpainting is more reliably. The experimental results show that the proposed algorithm over-comes the problem of excessive diffusion from high texture area to low area, and obtains better visual appearance.
Keywords:image inpainting  priority  local feature  curvature  gradient
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