The research of image inpainting algorithm using self-adaptive group structure and sparse representation

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Abstract

Focused on the issue that the object structure discontinuity and poor texture detail occurred in image inpainting method, the image inpainting algorithm based on self-adaptive group structure has proposed in this paper. The conception of self-adaptive group structure is different from traditional image patching operation and fixed group structure, which refers to the fact that a patch on the structure has fewer similar patches than the one within the textured region. A self-adaptive dictionary as well as the sparse representation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iterational operation. The experimental results on target removing with Criminisi’s algorithm, GSR’s algorithm and SALSA’s algorithm in image pixels losting of image inpainting had shown that the proposed algorithm has better performance than other algorithms.

Keywords

Image inpainting algorithm Sparse representation method Self-adaptive group structure Dictionary learning method 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61402053, No. 51408069), the Science and Technology Service Platform of Hunan Province (No. 2012TP1001).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Energy and Power EngineeringChangsha University of Science and TechnologyChangshaPeople’s Republic of China

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