Abstract
This passage puts forward a new sparse representation method, to solve the shortage problem of image restoration. First of all, extract the patch groups by utilize the non-local similar patches, and then using the simultaneous sparse coding to develop a non-local extension of Gaussian scale mixture model. Finally integrate the patch group model and Gaussian scale mixture model into encoding framework. Experimental results show that the proposed method achieves leading performance in terms of both quantitative measures and visual quality. In addition, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar methods.
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References
Zabukovec, A., Jaklič, J.: The impact of information visualisation on the quality of information in business decision-making. Int. J. Technol. Hum. Interact. 11(2), 61–79 (2015)
Lin, J.H., Peng, W.: The contributions of perceived graphic and enactive realism to enjoyment and engagement in active video games. Int. J. Technol. Hum. Interact. 11(3), 1–16 (2015)
Alamareen, A., Aljarrah, O., Aljarrah, I.A.: Image mosaicing using binary edge detection algorithm in a cloud-computing environment. Int. J. Inf. Technol. Web. Eng. 11(3), 1–14 (2016)
Wu, K., Kang, J., Chi, K.: Research on fault diagnosis method using improved multi-class classification algorithm and relevance vector machine. Int. J. Inf. Technol. Web. Eng. 10(3), 1–16 (2015)
Mathiyalagan, P., Suriya, S., Sivanandam, S.N.: Hybrid enhanced ant colony algorithm and enhanced bee colony algorithm for grid scheduling. Int. J. Grid Util. Comput. 2(1), 45–58 (2011)
Vinod, D.S., Mahesha, P.: Support vector machine-based stuttering dysfluency classification using GMM supervectors. Int. J. Grid Util. Comput. 6(3/4), 143–149 (2015)
Boyinbode, O., Le, H., Takizawa, M.: A survey on clustering algorithms for wireless sensor networks. Int. J. Space-Based Situated Comput. 1(2/3), 130–136 (2011)
Sun, N., Murakami, S., Nagaoka, H., et al.: A correction algorithm for stereo matching with general digital cameras and web cameras. Int. J. Space-Based Situated Comput. 3(3), 169–184 (2013)
Xu, J., Zhang, L., Zuo, W.: Patch group based nonlocal self-similarity prior learning for image denoising. In: Proceedings of the 15th IEEE International Conference on Computer Vision, pp. 244–252. Institute of Electrical and Electronics Engineers Inc., Santiago, Chile (2016)
Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)
Acknowledgments
This research was supported by National Natural Science Foundation of China (61471162, 61501178, 61601177); Program of International science and technology cooperation (2015DFA10940); Science and technology support program (R & D) project of Hubei Province (2015BAA115); PhD Research Startup Foundation of Hubei University of Technology (BSQD14028); Open Foundation of Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy (HBSKFZD2015005, HBSKFTD2016002).
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Lu, Y., Wu, M., Zhao, N., Liu, M., Liu, C. (2018). Gaussian Scale Patch Group Sparse Representation for Image Restoration. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_51
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DOI: https://doi.org/10.1007/978-3-319-59463-7_51
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