Abstract
Image inpainting is an approach of retaining the disfigured parts of the image in such a way that it looks acceptable to the naked eye. Image inpainting can be implemented by several techniques, wavelet-based, concept of variation and experience model. Here, in this article, we used the exemplar-based method, i.e. to copy and paste the texture from the known source region. The running time complexity is pretty higher for these techniques. So, a lot of techniques were introduced to improve confidence value and decrease time complexity. In this paper, a new hybrid algorithm is developed which mainly aims to increase the performance of restoration of the image by taking the information from nearby pixels and by selecting them with high probability pixels then replace them. The results achieved during the experiment show the efficiency of the hybrid algorithm. abstract environment.
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References
Criminisi, A., Perez, P., Toyama, K.: Region Filling and Object Removal by Exemplar Based Image Inpainting. IEEE Transactions on Image Processing 13(9), 1200–1212 (2004)
Vahid K. Aliliou and Farzin Yaghmaee, “Introducing A New Fast Exemplar-Based Inpainting Algorithm,” In The 22nd Iranian Conference on Electrical Engineering (ICEE 2014), May 20–22, 2014
Anupam, P. Goyal and S. Diwakar, “Fast and Enhanced Algorithm for Exemplar Bassed Image Inpaintinng,” In Fourth Pacific-Rim Symposium on Image and Video Technology (PSIVT), pp. 325–330, 2010
Patel, Jayesh, Sarode, Tanuja K.: “Exemplar based Image Inpainting with Reduced Search Region.” International Journal of Computer Applications 92(12), (April 2014)
A. Efros and T. Leung, “Texture synthesis by non parametric sampling,” In Proc. Int. Conf. Computer Vision, pages 1033–1038, Kerkyra, Greece, September 1999
M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, “Image inpainting,” in Proceedings of the 27th annual conference on Computer graphics and interactive techniques, 2000, pp. 417–424
Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: Simultaneous structure and texture image inpainting. IEEE Trans. Image Process. 12, 882–9 (2003)
Dobrosotskaya, A., Bertozzi, A.L.: A wavelet-Laplace variational technique for image deconvolution and inpainting. IEEE Trans. Image Process. 17, 657–663 (2008)
Levin, A., Zomet, A., Weiss, Y.: “Learning how to inpaint from global image statistics,” in IEEE Conf. Computer Vision and Pattern Recognition 305–312 (2003)
K. He and J. Sun, “Statistics of patch offsets for image completion,” in Computer Vision-ECCV 2012, ed: Springer, 2012, pp. 16–29
Kwok, T.-H., Sheung, H., Wang, C.C.: Fast query for exemplarbased image completion. IEEE Trans. Image Process. 19, 3106–3115 (2010)
Y. Wu and C. Yuan, “Fast and Robust Edge-Guided Exemplar-Based Image Inpainting,” in Image Analysis and Processing, ed: Springer, 2013, pp. 231–240
Yunqiang, L., Caselles, V.: Exemplar-Based Image Inpainting Using Multiscale Graph Cuts. IEEE Trans. Image Process. 22, 1699–1711 (2013)
Ding Ding, Sundaresh Ram, Jeffrey J. Rodríguez, Image Inpainting Using Nonlocal Texture Matching and Nonlinear Filtering, IEEE Transactions on Image Processing Year: 2019 | Volume: 28, Issue: 4 , 1705–1719
Uğur Erkan;Serdar Enginoğlu;Dang N.H. Thanh, An Iterative Image Inpainting Method Based on Similarity of Pixels Values 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE) Year: 2019, 18–19 Dec. 2019, https://doi.org/10.1109/ICSPIS48872.2019.9066140
Ang Li;Jianzhong Qi;Rui Zhang;Ramamohanarao Kotagiri, Boosted GAN with Semantically Interpretable Information for Image Inpainting 2019 International Joint Conference on Neural Networks (IJCNN), Year: 2019 | Date of Conference: 14–19 July 2019 https://doi.org/10.1109/IJCNN.2019.8851926
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Pavuluri, R., Abhinav, T., Kumar, P., Reza, M. (2022). New Hybrid Approach for Exemplar-Based Image Inpainting. In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition . Advances in Intelligent Systems and Computing, vol 1349. Springer, Singapore. https://doi.org/10.1007/978-981-16-2543-5_38
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DOI: https://doi.org/10.1007/978-981-16-2543-5_38
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