Abstract
We propose a super-resolution method that exploits self-similarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algorithms, we employ a method that generates image pairs directly from the image pyramid of one single frame. The generated patch pairs are clustered for training a dictionary by enforcing group sparsity constraints underlying the image patches. Super-resolution images are then constructed using the learned dictionary. Experimental results show the proposed method is able to achieve the state-of-the-art performance.
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
Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: A technical overview. IEEE Signal Processing Magazine, 21–36 (2003)
Morse, B., Schwartzwald, D.: Image magnification using level set reconstruction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 333–341 (2001)
Fattal, R.: Image upsampling via imposed edge statistics. In: SIGGRAPH 2007: ACM SIGGRAPH 2007 papers. ACM, New York (2007)
Irani, M., Peleg, S.: Improving resolution by image registration. Computer Vision, Graphics and Image Processing 53, 231–239 (1991)
Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 83–97 (2004)
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Computer Graphics and Applications, 56–65 (2002)
Sun, J., Zheng, N.N., Tao, H., Shum, H.Y.: Image hallucination with primal sketch priors. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 729–736 (2003)
Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 275–282 (2004)
Sun, J., Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2008)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation of raw image patches. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2008)
Xiong, X., Sun, X., Wu, F.: Image hallucination with feature enhancement. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2009)
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 349–356 (2009)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54, 4311–4322 (2006)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2272–2279 (2009)
Arya, S., Mount, D.M.: Approximate nearest neighbor queries in fixed dimensions. In: SODA 1993: Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete algorithms, pp. 271–280 (1993)
Berg, E.v., Friedlander, M.P.: SPGL1: A solver for large-scale sparse reconstruction (2007), http://www.cs.ubc.ca/labs/scl/spgl1
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: Proceedings of IEEE International Conference on Computer Vision, pp. 416–423 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, CY., Huang, JB., Yang, MH. (2011). Exploiting Self-similarities for Single Frame Super-Resolution. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-19318-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19317-0
Online ISBN: 978-3-642-19318-7
eBook Packages: Computer ScienceComputer Science (R0)