The research of image inpainting algorithm using self-adaptive group structure and sparse representation
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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.
KeywordsImage inpainting algorithm Sparse representation method Self-adaptive group structure Dictionary learning method
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).
- 5.Wong, A., Orchard, J.: A nonlocal-means approach to exemplar-based inpainting. Proceedings of the 15th IEEE International Conference on Image Processing. IEEE, 2008, pp. 2600–2603 (2008)Google Scholar
- 7.Shen, B., Hu, W., Zhang, Y., et al.: Image inpainting via sparse representation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009, pp. 697–700 (2009)Google Scholar
- 10.Mairal, J., Bach, F., Ponce, J.m et al.: Non-local sparse models for image restoration. Proceedings of the IEEE 12th International Conference on Computer Vision. IEEE, 2009, pp. 2272–2279 (2009)Google Scholar
- 12.Xu, J., Zhang, L., Zuo, W., et al.: Patch group based non-local self-similarity prior learning for image de-noising. Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 244–252 (2015)Google Scholar