Robust Impulse Noise Variance Estimation Based on Image Histogram

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.