Robust Impulse Noise Variance Estimation Based on Image Histogram

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Abstract
The state of the art impulse noise removal methods make use of the noise variance, or equivalently the noise mixing probability p, and are iterative procedures (e.g., , ). However, so far there has been a lack of effective estimator for p. As a result, true values of p are often used during simulation, which may not be practical. Furthermore, the optimal stopping criteria for the iterative algorithms have been elusive until recently. In a computationally heavy method is proposed for determining the optimal number of iterations. In this letter we make two contributions. We first develop a robust estimator for p by using the empirical observation that a natural image usually doesn't cover all pixel value range, then we design an efficient linear transformation to replace complicated computation of order statistics. Based on this estimated p value, we further derive the formula for estimating the true image histogram, and use it to formulate a new efficient optimal stopping criterion during the iterative denoising process. This formulation has a simple interpretation of its optimality and yields improved denoising performance.

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