State-conditioned rank-ordered filtering for removing impulse noise in images

A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable. As part of this state-based framework, several sliding-window algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the current state is computed according to the output of a simple classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in the current window. Based on the value of the state variable, the algorithm switches between the output of an identity filter and an order-statistic (OS) filter. For a small additional cost in memory, this simple strategy is easily generalized into a multi-state approach using weighted combinations of the identity and OS filters in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as thirty percent impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise