Abstract
In the field of image process research, super-resolution algorithm is very important and widely used in practice. Some traditional super-resolution algorithms run fast and keep a consistent structure between the original image and super-resolution result, but cannot rebuild the detail information. Other deep learning algorithms can achieve better results, but are time-consume. We propose a new super-resolution algorithm based on content regional division. According to the similarity of image content, the image is divided into several parts. If patches of these areas can match with the patches in the database, the corresponding algorithm is used to replace them; otherwise, the self-similar method is used for super-resolution processing. The experiment shows that we can get acceptable high-resolution images at higher speed.
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References
Xin, L.I., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001). https://doi.org/10.1109/83.951537
Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoustics Speech Sig. Process. 29(6), 1153–1160 (1981)
Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graph. Models Image Process. 53(3), 231–239 (1991). https://doi.org/10.1016/1049-9652(91)90045-L
Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 30(2), 12–11211 (2011). https://doi.org/10.1145/1944846.1944852
Cheeseman, P., Kanefsky : Subpixel resolution from multiple images 25, 241 (1994)
Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process. 5(6), 996–1011 (1996). https://doi.org/10.1109/83.503915
Fattal, R.: Image up-sampling via imposed edge statistics. ACM Trans. Graph 26(3) (2007). https://doi.org/10.1145/1276377.1276496
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. CoRR abs/1501.00092 (2015). 1501.00092
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. CoRR abs/1608.00367 (2016). 1608.00367
Shi, W., Caballero, J., Husz´ar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. CoRR abs/1609.05158 (2016). 1609.05158.
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput Graph Appl 22(2), 56–65 (2002). https://doi.org/10.1109/38.988747
Barnes, C., S, E., Finkelstein, A., G, D.B.: Patchmatch: A randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24–12411 (2009)
HaCohen, Y., Shechtman, E., Goldman, D.B., L, D.: Non-rigid dense correspondence with applications for image enhancement. ACM Trans. Graph. 30(4), 70–17010 (2011)
Z W, Bovik, A.C., Sheikh, H.R., Sim, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Zhang, L., Zhang, L., Mou, X.: Fsim: a feature similarity index for image quality assessment. Image Process. IEEE Trans. 20, 2378–2386 (2011)
Acknowledgements
The research work described in this paper was fully supported by the National Key R & D program of China (2017YFC1502505) and the Joint Research Fund in Astronomy (U2031136) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS). Professor Xin Zheng and Qian Yin are the authors to whom all correspondence should be addressed.
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Liang, X., Zhou, K., Lv, C., Zheng, X., Yin, Q. (2021). A Super Resolution Algorithm Based On Content Regional Division. In: Huang, C., Chan, YW., Yen, N. (eds) 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems. Advances in Intelligent Systems and Computing, vol 1379 . Springer, Singapore. https://doi.org/10.1007/978-981-16-1726-3_126
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DOI: https://doi.org/10.1007/978-981-16-1726-3_126
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