MEDIAN AND NEURAL-NETWORK HYBRID (MNNH) FILTERS

When a nonstationary signal containing sudden changes, such as an imag e signal, is degraded by an additive noise, a powerful means to recove r the signal is to use a nonlinear filter. This paper uses a layered n eural network, and proposes a new method to construct a nonlinear filt er for restoring a signal corrupted by mixed noise (i.e., mixed noise composed of Gaussian noise and impulse noise). As the first step, a pr ototype filter is proposed, which is a combination of a median filter and a linear (averaging) filter. Then, it is shown that the prototype filter can be represented by a network structure. By interpreting the network representation by a layered neural network, the idea is extend ed to the median neural network hybrid (MNNH) filter. The MNNH filter can be trained by the back-propagation algorithm. The prototype filter already has a high mixed noise elimination performance, but the MNNH filter can further improve the performance by reflecting information o n the signal to be processed (the original image and the additive nois e) when such information is given. Lastly, the usefulness of the MNNH filter is demonstrated through various application examples. (C) 1998 Scripta Technica.