Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness

Signal, Image and Video Processing

Volumn 10, Issue 3, 2016, Pages 535-542

  Yeganli, F ^a   Nazzal, M ^a   Unal, M ^a   Ozkaramanli, H ^a  

^a EASTERN MEDITERRANEAN UNIVERSITY   (Turkey)

Author keywords

Dictionary learning; Multiple dictionary pairs; Sharpness measure based clustering; Single image super resolution; Sparse representation

Indexed keywords

ALGORITHMS; OPTICAL RESOLVING POWER;

BASED CLUSTERING; DICTIONARY LEARNING; MULTIPLE DICTIONARIES; SINGLE IMAGES; SPARSE REPRESENTATION;

EDGE DETECTION;

EID: 84958107191     PISSN: 18631703     EISSN: 18631711     Source Type: Journal    
DOI: 10.1007/s11760-015-0771-7     Document Type: Article

References (24)

1. Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, 895–900 (2006)
2. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
3. Skretting, K., Engan, K.: Image compression using learned dictionaries by RLS-DLA and compared with K-SVD. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1517–1520 (2011)
4. Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)
5. Rubinstein, R., Alfred, M.B., Elad, M.: Dictionaries for sparse representation modeling. Proc. IEEE 98(6), 1045–1057 (2010)
6. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861–2873 (2010)
7. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse representations. Curves Surf. 6920, 711–730 (2010)
8. 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), 1838–1857 (2011)
9. Thanou, D., Shuman, D.I., Frossard, P.: Parametric dictionary learning for graph signals. In: Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 487–490 (2013)
10. Mallat, S., Yu, G.: Super-resolution with sparse mixing estimators. IEEE Trans. Image Process. 19(11), 2889–2900 (2010)
11. Feng, J., Song, L., Yang, X., Zhang, W.: Learning dictionary via subspace segmentation for sparse representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (ICIP), pp. 1245–1248 (2011)
12. Yu, G., Sapiro, G., Mallat, S.: Image modeling and enhancement via structured sparse model selection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (ICIP), pp. 1641–1644 (2010)
13. Kumar, J., Chen, F., Doermann, D.: Sharpness estimation for document and scene images. In: 21st International Conference on Pattern Recognition (ICPR), pp. 3292–3295 (2012)
14. He, L., Qi, H., Zaretzki, R.: Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 345–352 (2013)
15. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall Inc, Englewood Cliffs, NJ (2002)
16. Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition (ICIP), pp. 1–8 (2008)
17. http://see.xidian.edu.cn/faculty/wsdong/wsdong_downloads.htm
18. Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
19. Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(6), 2569–2582 (2014)
20. Elad, M.: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer, Berlin (2010)
21. http://www.cs.technion.ac.il/elad/software/
22. Kodak lossless true color image suite. http://r0k.us/graphics/kodak/
23. 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 the IEEE International Conference on Computer Vision (ICCV), pp. 416–423 (2001)
24. 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)