DocumentCode
2726015
Title
Minimum Noiseless Description Length (MNDL) Thresholding
Author
Fakhrzadeh, Azadeh ; Beheshti, Soosan
Author_Institution
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, Ont.
fYear
2007
fDate
1-5 April 2007
Firstpage
146
Lastpage
150
Abstract
In this paper, a new thresholding approach for data denoising is presented. The approach is based minimum noiseless description length (MNDL), a new method for optimum sub-space selection in data representation. By using the observed noisy data, this information theoretic approach provides the optimum threshold that minimizes the description length of the noiseless signal. Comparison of the new method with the existing thresholding methods is provided
Keywords
signal denoising; data denoising; data representation; information theory; minimum noiseless description length thresholding; noiseless signal; optimum subspace selection; Additive noise; Computational intelligence; Data engineering; Image reconstruction; Noise reduction; Random variables; Signal denoising; Signal processing; Signal restoration; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence in Image and Signal Processing, 2007. CIISP 2007. IEEE Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0707-9
Type
conf
DOI
10.1109/CIISP.2007.369308
Filename
4221409
Link To Document