A Blind Estimation for Speckle Noise Based on Gaussian-Hermite Moments
Miao Ma, Zheng Xue, Zhao Pei · 2016
As a kind of multiplicative noise, Speckle often degrades image quality. To estimate speckle noise helps to image restoration and consequent analysis. This paper suggests a Gaussian-Hermite moments based method to evaluate the variance of speckle. In the method, the characteristics of Gaussian-Hermite moments are discussed first. Then speckle noise with different variances are respectively added to a certain image where all the greyscales of pixels are the same, and the distribution of feature vectors based on Gaussian-Hermite moments is employed to analyze the noise intensity. Next, a conception of noise characteristic value is used to stand for noise intensity and herefrom construct a function mapping of noise variances and noise characteristic values, with which an estimate function is finally established via a polynomial curve fitting. Experimental results indicate that the estimation function can rapidly and correctly provide with the intensity of speckle without any prior knowledge. Potential applications include noise assessment, image quality analysis, and guidance for noise reduction.