非局部学习字典的图像修复  

Image Inpainting Based on Non-local Learned Dictionary

作　　者：李民[1,2] 程建[1,3] 李小文[1] 乐翔[3] 

机构地区：[1]电子科技大学地表空间信息技术研究所,成都611731 [2]桂林空军学院科研部,桂林541003 [3]电子科技大学电子工程学院,成都611731

出　　处：《电子与信息学报》2011年第11期2672-2678,共7页Journal of Electronics & Information Technology

基　　金：国家973计划项目(2007CB714406);中国博士后基金特别资助项目(200902609);电子科技大学青年科技基金重点项目(JX0804)资助课题

摘　　要：该文提出一种新的基于学习的图像修复算法。与经典的稀疏表示模型不同,该文将非局部自相似图像块统一进行联合稀疏表示,训练高效的学习字典,并使自相似块间保持相同的稀疏模式。该方法既确保自相似块投影到稀疏空间后也具有相似性,也较好地保留了自相似块间的相关性信息,更有效地建立了它们的联合稀疏关联,并将这种关联作为先验知识来指导图像的修复。该算法使用大量自然图像样本来训练初始的过完备字典,既利用了样本图像的先验知识,又充分考虑了待处理图像本身的相关信息,自适应性强。通过对自然图像进行大﹑小范围图像修复和文字去除实验,该文方法均取得不错的修复效果。A novel learning-based image inpainting method is presented.As a further development of classical sparse representation model,the non-local self-similar patches are unified for joint sparse representation and learning dictionary,in which each element of the self-similar patches has the same sparse pattern.The method assures the self-similar patches possess similarity when projected on the sparse space,and efficiently builds the sparse association among them.This association is next taken as a priori knowledge for image inpainting.The paper uses numerous samples and non-local patches of input image to train overcomplete dictionary.The method not only takes into account the priori knowledge of samples,but also considers the non-local self-similar information of input image.Large and small region inpainting experiments and text removing experiments on natural images show the good performance of the method.

关 键 词：图像修复 学习字典 稀疏表示 联合稀疏近似 非局部 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]