Utilizing homotopy for single image superresolution

1st Asian Conference on Pattern Recognition, ACPR 2011

Volumn , Issue , 2011, Pages 316-320

  Lu, Xiaoqiang ^a   Yan, Pingkun ^a   Yuan, Yuan ^a   Li, Xuelong ^a   Yuan, Haoliang ^b  

^a XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^b HUBEI UNIVERSITY   (China)

Author keywords

dictionary learning; homotopy; Superresolution

Indexed keywords

CRITICAL ISSUES; DICTIONARY LEARNING; HOMOTOPIES; HOMOTOPY METHOD; HOMOTOPY PATH; IMAGE SUPER-RESOLUTION; IS DESIGN; LEARNING METHODS; LEARNING STRATEGY; OVER-COMPLETE; RECONSTRUCTED IMAGE; SINGLE IMAGES; SPARSE REPRESENTATION; SUPER RESOLUTION; SUPER RESOLUTION ALGORITHMS; TERMINAL POINTS; TEST IMAGES; TRAINING IMAGE; VISUAL PERCEPTION;

OPTICAL RESOLVING POWER; PATTERN RECOGNITION;

IMAGE RECONSTRUCTION;

EID: 84862533677     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACPR.2011.6166654     Document Type: Conference Paper

References (13)

1. H. S. Hou and H. C. Andrews. "Cubic splines for image interpolation and digital filtering," IEEE Trans. Acoustics, Speech, Signal Process., 26(6): 508-517, 1978.
2. X. Li, M. T. Orchard. "New edge-directed interpolation," IEEE Trans. Image Process., 10(10): 1521-1527, 2001.
3. S. Dai, M. Han, et al. "Soft-cuts: a soft edge smoothness prior for color image super-resolution," IEEE Trans. Image Process., 18(5): 969-981, 2009.
4. R. C. Hardie, K. J. Barnard, et al. "Joint MAP registration and HR image estimation using a sequence of undersampled images," IEEE Trans. Image Process., 6(12): 1621-1633, 1997.
5. W. T. Freeman, E. C. Pasztor, et al. "Learning low-level vision," International Journal of Computer Vision, 40(1): 25-47, 2000.
6. H. Chang, D.Y. Yeung, et al. "Super-resolution through neighbor embedding," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1: 275-282, 2004.
7. J. Yang, J. Wright, T. Huang, and Y. Ma. "Image super-resolution via sparse representation," IEEE Trans. Image Process., 19(11): 2861-2873, 2010.
8. W. Dong, L. Zhang, et al. "Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization," accepted in IEEE Trans. Image Processing.
9. I. Daubechies, M. Defrise, et al. "An iterative thresholding algorithm for linear inverse problems with a sparsity constraint," Commun. Pure Appl. Math.,57(11): 1413-1457, 2004.
10. M. Aharon, M. Elad, and A.M. Bruckstein. "The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation," IEEE Trans. On Signal Processing, 54(11): 4311-4322, 2006.
11. S. Baker and T. Kanade, "Limits on super-resolution and how to break them," IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1167-1183, 2002.
12. H. Lee, A. Battle, R. Raina, and A. Y. Ng. "Efficient sparse coding algorithms," in Advances in Neural Information Processing Systems (NIPS), 2007.
13. A. Buades, B. Coll, and J. M. Morel. "A review of image denoising methods, with a new one," Multiscale Model. Simul, 4(2): 490-530, 2005.