非局部学习字典的图像修复

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。

Image Inpainting Based on Non-local Learned Dictionary

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 knowled...