Image Inpainting Based on Non-local Learned Dictionary
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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。Abstract: 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.
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