Abstract
This paper presents a novel single image super resolution method that reconstructs a super resolution image in an exemplar sub-space. The proposed method first synthesizes LR patches by perturbing the image formation model, and stores them in a dictionary. An SR image is generated by replacing the input image patchwise with an HR patch in the dictionary whose LR patch best matches the input. The abundance of the exemplars enables the proposed method to synthesize SR images within the exemplar sub-space. This gives numerous advantages over the previous methods, such as the robustness against noise. Experiments on documents images show the proposed method outperforms previous methods not only in image quality, but also in recognition rate, namely about 30% higher than the previous 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.
References
Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 1167–1183 (2002)
Datsenko, D., Elad, M.: Example-based single document image super-resolution: a global MAP approach with outlier rejection. Multidimensional Systems and Signal Processing 18, 103–121 (2007)
Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multi-frame super-resolution. IEEE Tran. on Image Processing 13, 1327–1344 (2004)
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Computer Graphics and Applications 22, 56–65 (2002)
Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graphical Models and Image Processing 53, 231–239 (1991)
Ng, M.K., Shen, H., Lam, E.Y., Zhang, L.: A total variation regularization based super-resolution reconstruction algorithm for digital video. EURASIP Journal on Advances in Signal Processing 74585 (2007)
Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 20, 21–36 (2003)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. on Image Processing 19, 2861–2873 (2010)
Zeyde, R., Elad, M., Protter, M.: On Single Image Scale-Up Using Sparse-Representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)
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
Shibata, T., Iketani, A., Senda, S. (2013). Single Image Super Resolution Reconstruction in Perturbed Exemplar Sub-space. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-37431-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37430-2
Online ISBN: 978-3-642-37431-9
eBook Packages: Computer ScienceComputer Science (R0)