DocumentCode :
2819450
Title :
Noise estimation using statistics of natural images
Author :
Zhai, Guangtao ; Wu, Xiaolin
Author_Institution :
ECE Dept., McMaster Univ., Hamilton, ON, Canada
fYear :
2011
fDate :
11-14 Sept. 2011
Firstpage :
1857
Lastpage :
1860
Abstract :
We develop a framework for estimating noises of natural images using two important properties of natural image statistics: high kurtosis and scale invariance of natural images in certain transform domains. We examine the effects of additive independent noise on the third and fourth moments of the transformed image signal (skewness and kurtosis). By exploring the said priors of high kurtosis and scale invariance of natural image statistics in 2D discrete cosine transform domain and random unitary transform domain, we derive constrained nonlinear optimization algorithms for accurate estimation of noise variance. Simulation and comparative study show that the proposed approach is capable of estimating the variance of Gaussian additive noise with a relative error as low as one percent. Moreover, the new estimation approach is shown to be effective on multiplicative-additive compound noises as well. This work can significantly improve the performance of existing denoising techniques that require the noise variance as a critical parameter.
Keywords :
Gaussian noise; discrete cosine transforms; image denoising; optimisation; 2D discrete cosine transform domain; Gaussian additive noise; constrained nonlinear optimization algorithms; kurtosis; multiplicative additive compound noises; natural image statistics; noise estimation; random unitary transform domain; scale invariance; transform domains; transformed image signal; Additives; Compounds; Discrete cosine transforms; Estimation; Indexes; Noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
ISSN :
1522-4880
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2011.6115828
Filename :
6115828
Link To Document :
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