Abstract
In this paper, we aim at producing the super-resolution image from a single low-resolution image based on Gaussian Process regression. Gaussian Processes provide a framework for deriving regression techniques with explicit uncertainty models. Super resolution can be transformed into a regression problem. We show how Gaussian Processes with covariance functions can be used for image super-resolution. Furthermore, considering that the training data have greatly effect on the super-resolution performance and the unsuitable training data would result in unexpected details, we adopt a data-driven scheme to learn a regression map for each query patch. There are two advantages of our approach: 1) we establish a map between the low-resolution space and the high-resolution space independent of a specified regression function; 2) the data-driven learning scheme improves the super-resolution performance. We estimate our approach on the popular testing images which are used in other super-resolution literatures, and the results demonstrate that our approach is efficient and it manifests a high-quality performance compared with several popular super-resolution methods.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Xin, L., Orchard, M.T.: New edge directed interpolation. In: Processing International Conference, pp. 311–314 (2000)
Schultz, R.R., Stevenson, R.L.: A Bayesian approach to image expansion for improved definition. IEEE Trans. on Image Processing 3, 233–242 (1994)
Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: CVPR 2008, Anchorage, AK, United states (2008)
Hou, H.S., Andrews, H.: Cubic splines for image interpolation and digital filtering. IEEE Trans. on Acoustics, Speech and Signal Processing 26, 508–517 (1978)
Tam, W.S., Kok, C.W., Siu, W.C.: Modified edge-directed interpolation for images. Journal of Electronic Imaging 19, 013011-013011-20 (2010)
Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. on PAMI 24, 1167–1183 (2002)
Irani, M., Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation 4, 324–335 (1993)
Kwok-Wai, H., Wan-Chi, S.: New motion compensation model via frequency classification for fast video super-resolution. In: Image Processing, pp. 1193–1196 (2009)
Morse, B.S., Schwartzwald, D.: Image magnification using level-set reconstruction. In: CVPR 2001, vol. 1, pp. 333–340 (2001)
Shan, Q., Li, Z., Jia, J., Tang, C.-K.: Fast image/video upsampling. ACM Transactions on Graphics, 153 (2008)
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Computer Graphics and Applications 22, 56–65 (2002)
Hong, C., Dit-Yan, Y., Yimin, X.: Super-resolution through neighbor embedding. In: CVPR 2004, vol. 1, pp. 275–282 (2004)
Liu, C., Shum, H.Y., Freeman, W.T.: Face hallucination: Theory and practice. International Journal of Computer Vision 75, 115–134 (2007)
Qiang, W., Xiaoou, T., Shum, H.: Patch based blind image super resolution. In: Computer Vision, vol. 1, pp. 709–716 (2005)
Jianchao, Y., Wright, J., Huang, T., Yi, M.: Image super-resolution as sparse representation of raw image patches. In: CVPR 2008, pp. 1–8 (2008)
Ni, K.S., Nguyen, T.Q.: Image superresolution using support vector regression. IEEE Trans. on Image Processing 16, 1596–1610 (2007)
He, H., Wan-Chi, S.: Single image super-resolution using Gaussian process regression. In: CVPR 2011, pp. 449–456 (2011)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. on Image Processing 19, 2861–2873 (2010)
Kwang In, K., Younghee, K.: Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior. IEEE Trans. on PAMI 32(6), 1127–1133 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qu, YY., Liao, MJ., Zhou, YW., Fang, TZ., Lin, L., Zhang, HY. (2013). Image Super-Resolution Based on Data-Driven Gaussian Process Regression. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-42057-3_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
eBook Packages: Computer ScienceComputer Science (R0)