Robust coding over noisy overcomplete channels

IEEE Transactions on Image Processing

Volumn 16, Issue 2, 2007, Pages 442-452

  Doi, Eizaburo ^a   Balcan, Doru C ^b   Lewicki, Michael S ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b Carnegie Mellon University   (United States)

Author keywords

Channel capacity constraint; Channel noise; Mean squared error (MSE) bounds; Overcomplete representations; Robust coding

Indexed keywords

CHANNEL CAPACITY; IMAGE CODING; INDEPENDENT COMPONENT ANALYSIS; MEAN SQUARE ERROR; PRINCIPAL COMPONENT ANALYSIS; WAVELET ANALYSIS;

CHANNEL NOISE; HIGH-DIMENSIONAL IMAGE DATA; ROBUST CODING;

CHANNEL CODING;

ALGORITHM; ARTICLE; ARTIFACT; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; METHODOLOGY; SIGNAL PROCESSING;

ALGORITHMS; ARTIFACTS; DATA COMPRESSION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 33847765051     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2006.888352     Document Type: Article

References (14)

1. A. Borst and F. E. Theunissen, "Information theory and neural coding," Nature Neurosci., vol. 2, pp. 947-957, 1999.
2. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
3. B. A. Olshausen and D. J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, vol. 381, pp. 607-609, 1996.
4. M. S. Lewicki and B. A. Olshausen, "Probabilistic framework for the adaptation and comparison of image codes," J. Opt. Soc. Amer. A, vol. 16, pp. 1587-1601, 1999.
5. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
6. D. J. Field, "Relations between the statistics of natural images and the response properteis of cortical cells," J. Opt. Soc. Amer. A, vol. 4, pp. 2379-2394, 1987.
7. E. Doi and M. S. Lewicki, "Sparse coding of natural images using an overcomplete set of limited capacity units," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2005, vol. 17, pp. 377-384.
8. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization. San Diego, CA: Academic, 1981.
9. J. J. Atick and A. N. Redlich, "What does the retina know about natural scenes?," Neural Comput., vol. 4, pp. 196-210, 1992.
10. K. I. Diamantaras and S. Y. Kung, "Channel noise and hidden units," in Principal Component Neural Networks: Theory and Applications. New York: Wiley, 1996, pp. 122-145.
11. K. I. Diamantaras, K. Hornik, and M. G. Strintzis, "Optimal linear compression under unreliable representation and robust PCA neural models," IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1186-1195, Oct. 1999.
12. I. Daubechies, "Discrete wavelet transforms: Frames," in Ten Lectures on Wavelets. Philadelphia, PA: SIAM, 1992, pp. 53-105.
13. V. K. Goyal, J. Kovačević, and J. A. Kelner, "Quantized frame expansions with erasures," Appl. Comput. Harmon. Anal., vol. 10, pp. 203-233, 2001.
14. G. H. Golub and C. F. van Loan, Matrix Computation, 3rd ed. Baltimore, MD: Johns Hopkins Univ. Press, 1996.