Image denoising via improved sparse coding

BMVC 2011 - Proceedings of the British Machine Vision Conference 2011

Volumn , Issue , 2011, Pages

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

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

^b HUBEI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; IMAGE DENOISING;

DICTIONARY LEARNING; K-MEANS METHOD; MATHEMATICAL DERIVATION; NOISE INFLUENCE; NOISE INFORMATION; OVER-COMPLETE DICTIONARIES; SPARSE CODING; SPARSE REPRESENTATION;

IMAGE CODING;

EID: 84898444501     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.110.310     Document Type: Conference Paper

References (16)

1. 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.
2. C. Kervrann and J. Boulanger. Optimal spatial adaptation for patch-based image denoisng. IEEE Trans. Image Process., 15(10): 2866-2878, Oct. 2006.
3. H. Takeda, S. Farsiu, and P. Milanfar. Kernel regression for image processing and reconstruction. IEEE Trans. Image Process., 16(2): 349-366, Feb. 2007.
4. M. Elad and M. Aharon. Image denosing via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process., 15(12): 3736-3745, Dec. 2006.
5. P. Chatterjee and P. Milanfar. Clustering-based denoising with locally learned dictionaries. IEEE Trans. Image Process., 18(7): 1438-1451, Jul. 2009.
6. K. Dabov, A. Foi, V. Katkovnik, and K. O. Egiazarian. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process., 16(8): 2080-2095, Aug. 2007.
7. B. Olshausen and D. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vis. Res. 37: 311-325, 1997.
8. K. Engan, S. O. Aase, and J. H. Hakon-Husoy. Method of optimal directions for frame design. In IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 5: 2443-2446, 1999.
9. K. Kreutz-Delgado and B. D. Rao. Focuss-based dictionary learning algorithms. In Wavelet Applications in Signal and Image Processing VIII, 2000.
10. K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski. Dictionary learning algorithms for sparse representation. Neur. Comput., 15(2): 349-396, 2003.
11. M. S. Lewicki and T. J. Sejnowski. Learning overcomplete representations. Neur. Comput., 12: 337-365, 2000.
12. L. Lesage, R. Gribonval, F. Bimbot, and L. Benaroya. Learning unions of orthonormal bases with thresholded singular value decomposition. In IEEE Intl Conf. Acoustics, Speech, and Signal Processing, 2005.
13. Y. Tang, P. Yan, Y. Yuan, X. Li, Single-Image Super-resolution via Local Learning, International Journal of Machine Learning and Cybernetics, 2(1): 15-23, 2011.
14. M. Aharon, M. Elad, and A. M. Bruckstein. On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them. J. Linear Algebra Appl., 416(1): 48-67, Jul. 2006.
15. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In Advances in Neural Information Processing Systems (NIPS), 2007.
16. J. F. Murray and A. C. Bovik. Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Processing Magazine, 26(1): 324-335, 1993.