Abstract
Image super-resolution aims to recover a visually pleasing high resolution image from one or multiple low resolution images. It plays an essential role in a variety of real-world applications. In this paper, we propose a novel hybrid example-based single image super-resolution approach which integrates learning from both external and internal exemplars. Given an input image, a proxy image with the same resolution as the target high-resolution image is first generated from a set of externally-learnt regression models. We then perform a coarse-to-fine gradient-level self-refinement on the proxy image guided by the input image. Finally, the refined high-resolution gradients are fed into a uniform energy function to recover the final output. Extensive experiments demonstrate that our framework outperforms the recent state-of-the-art single image super-resolution approaches both quantitatively and qualitatively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boulanger, J., Kervrann, C., Bouthemy, P.: Space-time adaptation for patch-based image sequence restoration. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1096–1102 (2007)
Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13, 1327–1344 (2004)
Protter, M., Elad, M., Tekeda, H., Milanfar, P.: Generalizing the non-local-means to super-resolution reconstruction. IEEE Trans. Image Process. 18, 36–51 (2009)
Shi, B., Zhao, H., Ben-Ezra, M., Yeung, S.-K., Fernandez-Cull, C., Shepard, R.H., Barsi, C., Raskar, R.: Sub-pixel layout for super-resolution with images in the octic group. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 250–264. Springer, Heidelberg (2014)
Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10, 1521–1527 (2001)
Su, D., Willis, P.: Image interpolation by pixel-level data-dependent triangulation. Comput. Graph. Forum 23, 189–201 (2004)
Tai, Y., Liu, S., Brown, M., Lin, S.: Super resolution using edge prior and single image detail synthesis. In: CVPR (2010)
Aly, H.A., Dubois, E.: Image up-sampling using total-variation regularization with a new observation model. IEEE Trans. Image Process. 14, 1647–1659 (2005)
Shan, Q., Li, Z., Jia, J., Tang, C.K.: Fast image/video upsampling. In: ACM SIGGRAPH Asia (2008)
Fattal, R.: Image upsampling via imposed edge statistics. In: ACM SIGGRAPH (2007)
Sun, J., Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: CVPR (2008)
Huang, J., Mumford, D.: Statistics of natural images and models. In: CVPR (1999)
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comput. Vis. 40, 25–47 (2000)
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. Comput. Graph. Appl. 22, 56–65 (2002)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: CVPR (2008)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861–2873 (2010)
HaCohen, Y., Fattal, R., Lischinski, D.: Image upsampling via texture hallucination. In: ICCP (2010)
Sun, J., Zhu, J., Tappen, M.F.: Context-constrained hallucination for image super-resolution. In: CVPR (2010)
Timofte, R., Smet, V.D., Gool, L.V.: Anchored neighborhood regression for fast example-based super-resolution. In: ICCV (2013)
Zhu, Y., Zhang, Y., Yuille, A.L.: Single image super-resolution using deformable patches. In: CVPR (2014)
Yang, C.Y., Yang, M.H.: Fast direct super-resolution by simple functions. In: ICCV (2013)
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: ICCV (2009)
Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 28, 1–10 (2010)
Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: CVPR (2015)
Yang, J., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: CVPR (2013)
Zontak, M., Irani, M.: Internal statistics of a single natural image. In: CVPR (2011)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV (2001)
Bevilacqua, M., Roumy, A., Guillemot, C., Morel, M.A.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: BMVC (2012)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. Curves Surf. 6920, 711–730 (2010)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 184–199. Springer, Heidelberg (2014)
Acknowledgments
This work was supported in part by ONR grant N000141310450 and NSF grants EFRI-1137172, IIP-1343402.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xian, Y., Yang, X., Tian, Y. (2015). Hybrid Example-Based Single Image Super-Resolution. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-27863-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27862-9
Online ISBN: 978-3-319-27863-6
eBook Packages: Computer ScienceComputer Science (R0)