An effective algorithm for noise variance estimation in shearlet domain

Abstract

The de-noising effect of many methods depends on the accuracy of the noise variance estimation. In this paper, we propose an effective algorithm for the noise variance estimation in shearlet domain. Firstly, the noisy image is decomposed into the low-frequency sub-band coefficients and multi-directional high-frequency sub-band coefficients based on the shearlet transform. Secondly, based on the high-frequency sub-band coefficients, the value of the noise variance is estimated using the Median Absolute Deviation (MAD) method. Thirdly, we choose some variance candidates in the neighborhood of the estimated value, and calculate the Residual Autocorrelation Power (RAP) of every variance candidate based on the Bayesian maximum a posteriori estimation (MAP) method. Finally, the accuracy of the noise variance estimation is improved using the residual autocorrelation power. A range of experiments demonstrate that the proposed method outperforms the traditional MAD method. The accuracy of the noise variance estimation has increased by 91.2% compared with the MAD method.