The Examplar-based Image Inpainting algorithm through Patch Propagation
Pranali Dhabekar1, Geeta Salunke2
1Pranali Dhabekar, Department of Electronics and Telecommunication, Pune University, Genba Sopanrao Moze College of Engineering, Balewadi, Pune (M.H), India.
2Geeta Salunke, Department of Electronics and Telecommunication, Pune University, Genba Sopanrao Moze College of Engineering, Balewadi, Pune (M.H), India.
Manuscript received on 18 October 2012 | Revised Manuscript received on 25 October 2012 | Manuscript published on 30 October 2012 | PP: 1-5 | Volume-1 Issue-4, October 2012 | Retrieval Number: C0300081312/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a novel and efficient examplar-based inpainting algorithm through investigating the sparsity of natural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priority and patch representation, which are two crucial steps for patch propagation in the examplar-based inpainting approach. First, patch structure sparsity is designed to measure the confidence of a patch located at the image structure (e.g., the edge or corner) by the sparseness of its nonzero similarities to the neighboring patches. The patch with larger structure sparsity will be assigned higher priority for further inpainting. Second, it is assumed that the patch to be filled can be represented by the sparse linear combination of candidate patches under the local patch consistency constraint in a framework of sparse representation. Compared with the traditional examplar-based inpainting approach, structure sparsity enables better discrimination of structure and texture, and the patch sparse representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures.
Keywords: Image Inpainting, Patch Propagation, Patch Sparsity, Sparse Representation, Texture Synthesis.
Scope of the Article: Image Processing