Abstract
We present a theoretical analysis and a new algorithm for the problem of super-resolution imaging: the reconstruction of HR (high-resolution) images from a sequence of LR (low-resolution) images. Super-resolution imaging entails solutions to two problems. One is the alignment of image frames. The other is the reconstruction of a HR image from multiple aligned LR images. Our analysis of the latter problem reveals insights into the theoretical limits of super-resolution reconstruction. We find that at best we can reconstruct a HR image blurred by a specific low-pass filter. Based on the analysis we present a new wavelet-based iterative reconstruction algorithm which is very robust to noise. Furthermore, it has a computationally efficient built-in denoising scheme with a nearly optimal risk bound. Roughly speaking, our method could be described as a better-conditioned iterative back-projection scheme with a fast and optimal regularization criteria in each iteration step. Experiments with both simulated and real data demonstrate that our approach has significantly better performance than existing super-resolution methods. It has the ability to remove even large amounts of mixed noise without creating smoothing artifacts.
Chapter PDF
References
Irani, M., Peleg, F.: Motion analysis for image enhancement: Resolution, occlusion and transparency. Journal of Visual Comm. and Image Repr. 4, 324–335 (1993)
Eland, M., Feuer, A.: Restoration of a signal super-resolution image from several blurred, noisy and undersampled measured images. IEEE Transaction on Image Processing, 1646–1658 (1997)
Bascle, B., Blake, A., Zisserman, A.: Motion deblurring and super-resolution from an image sequence. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 573–582. Springer, Heidelberg (1996)
Zhao, W., Sawhney, H.S.: Is super-resolution with optical flow feasible? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 599–613. Springer, Heidelberg (2002)
Tekalp, A., Ozkan, M., Sezan, M.: High-resolution image reconstruction from low-resolution image sequences and space-varying image restoration. In: ICASSP, pp. 169–172 (1992)
Farsiu, S., Robinson, D., Elad, M., Milanfar, P.: Robust shift and add approach to super-resolution. In: SPIE (2003)
Chambolle, A., Devore, R., Lee, N., Lucier, B.: Nonlinear wavelet image processing: variational problems, compression and noise removal through wavelets. IEEE Trans. Image Processing 7 (1998)
Youla, C.: Generalized image restoration by the method of alternating orthogonal projections IEEE Trans. Circuits Syst. 25 (1978)
Chan, R., Chan, T., Shen, L., Shen, Z.: Wavelet deblurring algorithms for spatially varying blur from high-resolution image reconstruction. Linear algebra and its applications 366, 139–155 (2003)
Nguyen, N., Milanfar, N.P.: An wavelet-based interpolation-restoration method for superresolution. Circuits, Systems and Signal Processing 19, 321–338 (2002)
Baker, S., Kanade, T.: Limits on super-resolution and how to break them. In: CVPR, pp. 372–379 (2000)
Mallat, S.: A wavelet tour of signal processing. Academic Press, London (1999)
Weickert, J.: Anisotropic Diffusion in Image Processing. ECMI Series, Teubner, Stuttgart (1998)
Mrazek, P., Weickert, J., Steidl, G.: Correspondences between wavelet shrinkage and nonlinear diffusion. In: Scale-Space, pp. 101–116 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ji, H., Fermüller, C. (2006). Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744085_23
Download citation
DOI: https://doi.org/10.1007/11744085_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33838-3
Online ISBN: 978-3-540-33839-0
eBook Packages: Computer ScienceComputer Science (R0)