Generalized homomorphic and adaptive order statistic filters for the removal of impulsive and signal-dependent noise

In this paper, we propose two new nonlinear filters for filtering signal-dependent noise, additive noise, and impulsive noise in image processing. The first filter proposed is an order statistic filter based on a generalized homomorphic transformation. The second is an adaptive order statistic filter with a variable threshold, which changes according to the noise level. Both of these filters perform well for the different kinds of noise encountered in image processing. They suppress signal-dependent noise, additive noise, and impulsive noise better than median filters, -trimmed mean filters, general nonlinear mean filters, modified trimmed mean filters, and double-window modified trimmed mean filters. They also preserve the edges of an image better than median filters and are simple to implement.