Title :
Noise estimation using statistics of natural images
Author :
Zhai, Guangtao ; Wu, Xiaolin
Author_Institution :
ECE Dept., McMaster Univ., Hamilton, ON, Canada
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;
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2011.6115828