Robust Impulse Noise Variance Estimation Based on Image Histogram
Yi Wan, Qiqiang Chen, Yan Qin Yang · IEEE Signal Processing Letters · 2010
The state of the art impulse noise removal methods make use of the noise variance, or equivalently the noise mixing probabilityp, and are iterative procedures (e.g., , ). However, so far there has been a lack of effective estimator forp. As a result, true values ofpare 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 forpby 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 estimatedpvalue, 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.