Abstract
Image super resolution (SR) is an active research topic to obtain an high resolution (HR) image from the low resolution (LR) observation. Many results of existing methods may be corrupted by some artifacts. In this paper, we propose an SR reconstruction method for single image based on nonlocal sparse and low-rank regularization. We form a matrix for each patch with its vectorized similar patches to utilize the redundancy of similar patches in natural images. This matrix can be decomposed as the low rank component and sparse part, where the low rank component depictures the similarity and the sparse part depictures the fine differences and outliers. The SR result is achieved by the iterative method and corroborated by experimental results, showing that our method outperforms other prevalent methods.
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References
Zhang, K., Gao, X., Tao, D., Li, X.: Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process. 21(11), 4544–4556 (2012)
Zhang, L., Wu, X.: An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process. 15(8), 2226–2238 (2006)
Li, M., Nguyen, T.Q.: Markov random field model-based edge-directed image interpolation. IEEE Trans. Image Process. 17(7), 1121–1128 (2008)
Marquina, A., Osher, S.J.: Image super-resolution by TV-regularization and bregman iteration. J. Sci. Comput. 37(3), 367–382 (2008)
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. Comput. Graphics Appl. 22(2), 56–65 (2002)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Chang, H., Yeung, D., Xiong, Y.: Super-resolution through neighor embedding. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 4, pp. 275–282 (2004)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 60–65 (2005)
Dong, W., Zhang, L., Shi, G., Wu, X.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20(7), 1836–1857 (2011)
Peyré, G., Bougleux, S., Cohen, L.: Non-local regularization of inverse problems. In: 10th European Conference on Computer Vision, pp. 57–68 (2008)
Peng, J., Hon, B.Y., Kong, D.: A structural low rank regularization method for single image super-resolution. Mach. Vis. Appl. 26(7–8), 991–1005 (2015)
Li, Q., Lu, Z., Sun, C., Li, H., Li, W.: Single image super-resolution based on nonlocal similarity and sparse representation. In: 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), pp. 156–160 (2015)
Dong, W., Shi, G., Hu, X., Ma, Y.: Nonlocal sparse and low-rank regularization for optical flow estimation. IEEE Trans. Image Process. 23(10), 4527–4538 (2014)
Huang, C., Ding, X., Fang, C., Wen, D.: Robust image restoration via adaptive low-rank approximation and joint kernel regression. IEEE Trans. Image Process. 23(7), 3085–3098 (2014)
Candes, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 11:1–11:37 (2011)
Yang, J., Wright, J., Huang, T., Ma, Y.: A singular value thresholding algorithm for matrix completion. Soc. Ind. Appl. Math. 20(4), 1956–1982 (2008)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Irani, M., Peleg, S.: Motion analysis for image enhancement: resolution, occlusion and transparency. J. Vis. Commun. Image Represent. 4(4), 324–335 (1993)
Yang, J., Wang, Z., Lin, Z., Cohen, S., Huang, T.: Coupled dictionary training for image super-resolution. IEEE Trans. Image Process. 21(8), 3467–3478 (2011)
Acknowledgement
This work is supported by the NSFC (No. 61273298), and Science and Technology Commission of Shanghai Municipality (No. 14DZ2260800).
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Liu, C., Fang, F., Xu, Y., Shen, C. (2016). Single Image Super-Resolution Based on Nonlocal Sparse and Low-Rank Regularization. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_21
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DOI: https://doi.org/10.1007/978-3-319-42911-3_21
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