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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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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