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

Abstract

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, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\alpha</tex> -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.