Higher-order statistical models of visual images

Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999

Volumn , Issue , 1999, Pages 54-57

  Simoncelli, Eero P ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PHOTOGRAPHY; SIGNAL PROCESSING;

DENSITY MODELS; EMPIRICAL DENSITY; HIGHER-ORDER; IMAGE CONTENT; NON-GAUSSIAN; PARAMETERIZED; PHOTOGRAPHIC IMAGE; VISUAL IMAGE;

HIGHER ORDER STATISTICS;

EID: 33645974717     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HOST.1999.778691     Document Type: Conference Paper

References (11)

1. A. J. Bell and T. J. Sejnowski. The 'independent components' of natural scenes are edge filters. Vision Research, 37(23):3327-3338, 1997.
2. R. W. Buccigrossi and E. P. Simoncelli. Image compression via joint statistical characterization in the wavelet domain. Technical Report 414, GRASP Laboratory, University of Pennsylvania, May 1997. Accepted (3/99) for publication in IEEE Trans Image Processing.
3. J. F. Cardoso. Source separation using higer order moments. In ICASSP, pages 2109-2112, 1989.
4. P. Common. Independent component analysis, a new concept? Signal Process., 36:387-314, 1994.
5. D. J. Field. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A, 4(12):2379-2394, 1987.
6. S. G. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Pat. Anal. Mach. Intell., 11:674-693, July 1989.
7. B. A. Olshausen and D. J. Field. Natural image statistics and efficient coding. Network: Computation in Neural Systems, 7:333-339, 1996.
8. J. Portilla and E. P. Simoncelli. Texture representation and synthesis using correlation of complex wavelet coefficient magnitudes. Technical Report 54, Consejo Superior de Investigaciones Cientificas (CSIC), Madrid, March 1999. Available at ftp://ftp.optica.csic.es/pub/papers/portillaTR54.ps.gz.
9. E. P. Simoncelli. Statistical models for images: Compression, restoration and synthesis. In 31st Asilomar Conf on Signals, Systems and Computers, pages 673-678, Pacific Grove, CA, November 1997. IEEE Computer Society.
10. E. P. Simoncelli. Bayesian denoising of visual images in the wavelet domain. In P. Müller and B. Vidakovic, editors, Bayesian Inference in Wavelet Based Models. Springer-Verlag, New York, To appear: Summer 1999.
11. E. P. Simoncelli and E. H. Adelson. Noise removal via Bayesian wavelet coring. In Third Int'l Conf on Image Proc, volume I, pages 379-382, Lausanne, September 1996. IEEE Sig Proc Society.