Abstract
Edge-directed single image super-resolution methods have been paid more attentions due to their sharp edge preserving in the recovered high-resolution image. Their core is the high-resolution gradient estimation. In this paper, we propose a novel cross-resolution gradient sharpening function learning to obtain the high-resolution gradient. The main idea of cross-resolution learning is to learn a sharpening function from low-resolution, and use it in high-resolution. Specifically, a blurred low-resolution image is first constructed by performing bicubic down-sampling and up-sampling operations sequentially. The gradient sharpening function considered as a linear transform is learned from blurred low-resolution gradient to the input low-resolution image gradient. After that, the high-resolution gradient is estimated by applying the learned gradient sharpening function to the initial blurred gradient obtained from the bicubic up-sampled of the low-resolution image. Finally, edge-directed single image super-resolution reconstruction is performed to obtain the sharpened high-resolution image. Extensive experiments demonstrate the effectiveness of our method in comparison with the state-of-the-art approaches.
J. Chu—This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61263046, 61403376 and 61175025).
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Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based superresolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)
Kim, K., Kwon, Y.: Example-based learning for singleimage SR and JPEG artifact removal, Max Planck’Insitut fur biologische Kybernetik, Tbingen, Germany Tech. Rep. 173 (2008)
Yang, J., Wright, J., Ma, Y., Huang, T.: Image super-resolution as sparse representation of raw image patches. In: Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 1–8, June 2008
Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proc. IEEE Conf. Comput. Vision Pattern Recognit., June–July 2004, pp. 275–282 (2004)
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: International Conference on Computer Vision, pp. 349–356 (2009)
Irani, M., Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion and transparency. J. Visual Commun. Image Representation 4(4), 324–335 (1993)
Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167–1183 (2002)
Lin, Z., Shum, H.-Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 83–97 (2004)
Tai, Y.-W., Liu, S., Brown, M.S., Lin, S.: Super resolution using edge prior and single image detail synthesis. In: Proc. IEEE Conf. Comput. Vision Pattern Recognit., June 2010, pp. 2400–2407 (2010)
Sun, J., Xu, Z., Shum, H.-Y.: Image super-resolution using gradient profile prior. In: Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 1–8 (2008)
Sun, J., Xu, Z., Shum, H.-Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. 20(6), 1529–1542 (2011)
Wang, L.F., Xiang, S.M., Meng, G.F., Wu, H.Y., Pan, C.H.: Edge-Directed Single-Image Super-Resolution Via Adaptive Gradient Magnitude Self-Interpolation. IEEE Trans. Circuits and Systems for Video Technology 23(8), 1289–1299 (2013)
Fattal, R.: Image upsampling via imposed edge statistics. ACM Trans. Graph. 26(3), 95:1–95:8 (2007)
Dai, S., Han, M., Xu, W., Wu, Y., Gong, Y.: Soft edge smoothness prior for alpha channel super resolution. In: Proc. IEEE Conf. Comput. Vision Pattern Recognit., June 2007, pp. 1–8 (2007)
Morse, B.S., Schwartzwald, D.: Image magnification using level set reconstruction. In: Proc. IEEE Conf. Comput. Vision Pattern Recognit., December 2001, pp. 333–340 (2001)
Fattal, R.: Image upsampling via imposed edge statistics. ACM Transactions on Graphics 26(3), 95:1–95:8 (2007). (Proceedings of SIGGRAPH 2007)
Shan, Q., Li, Z., Jia, J., Tang, C.-K.: Fast image/video upsampling. ACM Transactions on Graphics 27(7), 153:1–153:7 (2008). (SIGGRAPH ASIA)
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Han, W., Chu, J., Wang, L., Pan, C. (2015). Edge-Directed Single Image Super-Resolution via Cross-Resolution Sharpening Function Learning. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_21
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