Multi-example feature-constrained back-projection method for image super-resolution
Example-based super-resolution algorithms, which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive kNN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image. Experimental results demonstrate that our method improves the visual quality of the high-resolution image.
Keywordsfeature constraints back-projection super-resolution (SR)
The authors would like to thank the anonymous reviewers for giving valuable suggestions that greatly improved the paper. The authors also thank other researchers who provided the code for their algorithms for comparative testing. This project was supported by the National Natural Science Foundation of China (Grant Nos. 61572292, 61332015, 61373078, and 61272430), and the National Research Foundation for the Doctoral Program of Higher Education of China (Grant No. 20110131130004).
- Glasner, D.; Bagon, S.; Irani, M. Super-resolution from a single image. In: Proceedings of the IEEE 12th International Conference on Computer Vision, 349–356, 2009.Google Scholar
- Kolte, R.; Arora, A. Image super-resolution. Available at https://pdfs.semanticscholar.org/20de/2880a4196a733314252a717f1a55f5f0ea64.pdf.Google Scholar
- McKinley, S.; Levine, M. Cubic spline interpolation. College of the Redwoods Vol. 45, No. 1, 1049–1060, 1998.Google Scholar
- Irani, M.; Peleg, S. Improving resolution by image registration. CVGIP: Graphical Models and Image Processing Vol. 53, No. 3, 231–239, 1991.Google Scholar
- Dong, W.; Zhang, L.; Shi, G.; Wu, X. Nonlocal back-projection for adaptive image enlargement. In: Proceedings of the 16th IEEE International Conference on Image Processing, 349–352, 2009.Google Scholar
- Adelson, E. H.; Anderson, C. H.; Bergen, J. R.; Burt, P. J.; Ogden, J. M. Pyramid methods in image processing. RCA Engineer Vol. 29, No. 6, 33–41, 1984.Google Scholar
- Bevilacqua, M.; Roumy, A.; Guillemot, C.; Alberi-Morel, M. L. Low-complexity single-image superresolution based on nonnegative neighbor embedding. In: Proceedings of British Machine Vision Conference, 135.1–135.10, 2012.Google Scholar
- Yang, C.-Y.; Huang, J.-B.; Yang, M.-H. Exploiting self-similarities for single frame super-resolution. In: Computer Vision–ACCV 2010. Kimmel, R.; Klette, R.; Sugimoto, A. Eds. Springer Berlin Heidelberg, 497–510, 2010.Google Scholar
- Dong, W.; Shi, G.; Zhang, L.; Wu, X. Super-resolution with nonlocal regularized sparse representation. In: Proceedings of SPIE7744, Visual Communications and Image Processing, 77440H, 2010.Google Scholar
- Yang, J.; Wright, J.; Huang, T.; Ma, Y. Image super-resolution as sparse representation of raw image patches. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–8, 2008.Google Scholar
- Zhang, H.; Zhang, Y.; Huang, T. S. Efficient sparse representation based image super resolution via dual dictionary learning. In: Proceedings of the IEEE International Conference on Multimedia and Expo, 1–6, 2011.Google Scholar
- Chang, H.; Yeung, D.-Y.; Xiong, Y. Super-resolution through neighbor embedding. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, I, 2004.Google Scholar
- Church, K. W.; Helfman, J. I. Dotplot: A program for exploring self-similarity in millions of lines of text and code. Journal of Computational and Graphical Statistics Vol. 2, No. 2, 153–174, 1993.Google Scholar
- Shechtman, E.; Irani, M. Matching local selfsimilarities across images and videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–8, 2007.Google Scholar
- Caiming, Z.; Xin, Z.; Xuemei, L.; Fuhua, C. Cubic surface fitting to image with edges as constraints. In: Proceedings of the IEEE International Conference on Image Processing, 1046–1050, 2013.Google Scholar
- Freedman, G.; Fattal, R. Image and video upscaling from local self-examples. ACM Transactions on Graphics Vol. 30, No. 2, Article No. 12, 2011.Google Scholar
- Hore, A.; Ziou, D. Image quality metrics: PSNR vs. SSIM. In: Proceedings of the 20th International Conference on Pattern Recognition, 2366–2369, 2010.Google Scholar
Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.