Efficient single image super-resolution via graph-constrained least squares regression

Multimedia Tools and Applications

Volumn 72, Issue 3, 2014, Pages 2573-2596

  Jiang, Junjun ^a   Hu, Ruimin ^a   Han, Zhen ^a   Lu, Tao ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Graph embedding; Local geometric structure; Manifold learning; Super resolution

Indexed keywords

OPTICAL RESOLVING POWER;

ALGORITHMIC SOLUTIONS; CONSTRAINED LEAST SQUARES; GRAPH EMBEDDINGS; HIGH RESOLUTION IMAGE; LOCAL GEOMETRIC STRUCTURES; MANIFOLD LEARNING; STATE-OF-THE-ART METHODS; SUPER RESOLUTION;

GEOMETRY;

EID: 84879394662     PISSN: 13807501     EISSN: 15737721     Source Type: Journal    
DOI: 10.1007/s11042-013-1567-9     Document Type: Article

References (30)

1. Baker S, Kanade T (2002) Limits on super resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9):1167-1182
2. Belkin M (2003) Problems of learning on manifolds. PhD thesis, University of Chicago
3. Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems 14:585-591
4. Bevilacqua M, Roumy A, Guillemot C, Alberi Morel M-L (2012) Neighbor embedding based single image super-resolution using semi-nonnegative matrix factorization, In: IEEE International Conference on Acoustics and Signal Processing, pp. 1289-1292
5. Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel A (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding, In: British Machine Vision Conference, BMVC
6. Chan T-M, Zhang J, Pu J, Huang H (2009) Neighbor embedding based super-resolution algorithm through edge detection and feature selection. Pattern Recognit Lett 30:494-502
7. Chang H, Yeung D, Xiong Y (2004) Super-resolution through neighbor embedding, In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 275-282
8. Chopra A, Lian H (2010) Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images. Pattern Recognition 43:2609-2619
9. Chung FRK (1997) Spectral graph theory, volume 92 of regional conference series in mathematics. AMS 15(12):3736-3745
10. Daniel D, Lee H (1999) Sebastian Seung, learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788-791
11. Ding C, Li T, Jordan MI (2010) Convex and semi-nonnegative matrix factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(1):45-55
12. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 54(12):3736-3745
13. Fan W, Yeung DY (2007) Image hallucination using neighbor embedding over visual primitive mainfolds, In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-7
14. Farsiu S, Elad M, Milanfar P (2006) Multiframe demosaicing and super-resolution of color images. IEEE Transactions on Image Processing 15(1):141-159
15. Freeman W, Pasztor E, Carmichael O (2000) Learning low-level vision. Int J Comput Vis 40(1):25-47
16. Gao X, Zhang K, Tao D, Li X (2011) Joint learning for single image super-resolution via coupled constraint. IEEE Transactions on Image Processing 21(2):469-480
17. Lawson and Hanson (1974) Solving least squares problems, Prentice-Hall
18. Lin Z, Shum H (2004) Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1):83-97
19. Milanfar P (2010) Super-resolution imaging. CRC Press
20. Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 20(3):21-36
21. Rudin L, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D: Nonlinear Phenomena 60(1-4):259-268
22. Su, Tian Q, Sebe N, Ma J (2005) Neighborhood issue in single-frame image super-resolution, In: IEEE International Conference on Multimedia and Expo (ICME)
23. Tikhonov N, Arsenin VY (1977) Solutions of ill-posed problems. Winston, New York
24. Tsai RY, Huang TS (1984) Multiple frame image restoration and registration. In: Advances in computer vision and image processing. JAI, Greenwich, pp 317-339
25. van Ouwerkerk JD (2006) Image super-resolution survey. Image and Vision Computing 24:1039-1052
26. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error measurement to structural similarity. IEEE Transactions on Image Processing 13(4):600-612
27. Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches, In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8
28. Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation. IEEE Transactions on Image Processing 19(11):2861-2873
29. Zhang K, Gao X, Li X, Tao D (2011) Partially supervised neighbor embedding for example-based image super-resolution. IEEE Journal of Selected Topic in Signal Processing 5(2):230-239
30. Zibetti MVW, Bazán FSV, Mayer J (2011) Estimation of the parameters in regularized simultaneous super-resolution. Pattern Recognit Lett 32:69-78