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| Image Inpainting Based on Non-local Learned Dictionary |
| Li Min①③ Cheng Jian①② Li Xiao-wen① Le Xiang② |
①(Institute of Geo-Spatial Information Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China)
②(School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)
③(Department of Scientific Research, Guilin Airforce Academy, Guilin 541003, China) |
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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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Received: 27 December 2010
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Corresponding Authors:
Li Min
E-mail: gllm126@163.com
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2011, Vol. 33 
