Abstract
An efficient and robust super-resolution reconstruction algorithm for video sequences is proposed. In this algorithm, the L1 and L2 norms are introduced to form the data fusion term according to whether there exits motion estimation, and a robust Bilateral-TV regularization term is added to overcome the ill-posed problem of super-resolution estimation. Furthermore, we propose the use of regularization functional instead of a constant regularization parameter. The regularization functional is defined in terms of the reconstructed image at each iteration step, therefore allowing for the simultaneous determination of its value and the reconstruction of the super-resolution image. The iteration scheme, convexity and control parameter are thoroughly studied. Experimental results demonstrate the power of the proposed method.
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., Pak, M.K., Kang, M.G.: Super-resolution Image Reconstruction: a Technical Overview. IEEE Signal Processing Magazine 20(3), 21–36 (2003)
Nguyen, N., Milanfar, P., Golub, G.: A Computationally Efficient Superresolution Image Reconstruction Algorithm. IEEE Transactions on Image Processing 10(4), 573–583 (2001)
Elad, M., Hel-Or, Y.: A Fast Super-resolution Reconstruction Algorithm for Pure Translational Motion and Common Space Invariant Blur. IEEE Transactions on Image Processing 10(8), 1187–1193 (2001)
Shen, H., Zhang, L., Huang, B., et al.: A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution. IEEE Transactions on Image Processing 16, 479–490 (2007)
Altunbasak, Y., Patti, A.J., Mersereau, R.M.: Super-resolution Still and Video Reconstruction from MPEG-coded Video. IEEE Transactions on Circuits and Systems for Video Technology 12, 217–226 (2002)
Protter, M., Elad, M., Takeda, H., Milanfar, P.: Generalizing the Non-Local-Means to Super-Resolution Reconstruction. IEEE Transactions on Image Processing (to appear, 2008)
Farsiu, S., Robinson, D., Elad, M., Milanfar, P.: Fast and Robust Multi-frame Super-Resolution. IEEE Transactions on Image Processing 13(10), 1327–1344 (2004)
Banham, M.R., Katsaggelos, A.K.: Digital Image Restoration. IEEE Signal Processing Magazine 14(2), 24–41 (1997)
Golub, G.H., Heath, M., Wahba, G.: Generalized Cross-validation as a Method for Choosing a Good Ridge Parameter. Technometrics 21(2), 215–223 (1979)
Bose, N.K., Lertrattanapanich, S., Koo, J.: Advances in Superresolution using L-curve. In: The IEEE International Symposium on Circuits and Systems, Sydney, Australia, vol. 2, pp. 433–436 (2001)
Kang, M.G., Katsaggelos, A.K.: General Choice of the Regularization Functional in Regularized Image Restoration. IEEE Transactions on Image Processing 4(5), 594–602 (1995)
He, H., Kondi, L.P.: Resolution Enhancement of Video Sequences with Simultaneous Estimation of the Regularization Parameter. SPIE Journal of Electronic Imaging 13, 586–596 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, Y., Chen, R., Shu, F. (2009). An Efficient and Robust Algorithm for Improving the Resolution of Video Sequences. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_116
Download citation
DOI: https://doi.org/10.1007/978-3-642-01513-7_116
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
Online ISBN: 978-3-642-01513-7
eBook Packages: Computer ScienceComputer Science (R0)