Abstract
This paper proposes a novel noniterative orientation adaptive image inpainting algorithm. Assuming the image can be locally modeled, the filling process is formulated as a linear optimization problem, which the optimal coefficients can be adapted to match an arbitrary-oriented edge based on local geometric similarity. We provided A Weighted Least Square (WLS) method is provided to offer a convenient way of finding the optimal solution, which the weight function is selected based on the non local means. We also present Group Marching method (GMM) as the propagation scheme such that sharp edges are well propagated into the missing region layer by layer while maintaining the local geometric similarity. A number of examples on real and synthetic images demonstrate the effectiveness of our algorithm.
This work is supported by the National Science Fund for Distinguished Young Scholars (No.60525213) and the Key Project (Project No. 60533030) of NSFC, and 973 Program of China (Project No.2006CB303106).
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Qi, P., Luo, X., Zhu, J. (2009). An Image Inpainting Algorithm Based on Local Geometric Similarity. In: Huet, B., Smeaton, A., Mayer-Patel, K., Avrithis, Y. (eds) Advances in Multimedia Modeling . MMM 2009. Lecture Notes in Computer Science, vol 5371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92892-8_43
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DOI: https://doi.org/10.1007/978-3-540-92892-8_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92891-1
Online ISBN: 978-3-540-92892-8
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