Abstract
A nonlinear digital filter named as an extended component-separating filter (an ECS filter for short) is proposed for removing additive random noise from signals with abrupt changes, such as images and speech, while restoring the signal waveform as precise as possible. This filter corresponds to an extended version of nonlinear filters, such as an ε-filter and a component-separating filter (a CS filter for short) and realizes higher performance than these nonlinear filters. Especially, the ECS filter is effective for both a step-like signal and a continuously changing signal, while the ε-filter and the CS filter are effective for either of them. Since the filter structure corresponds to a layered neural network, the ECS filter can be optimized by a training method as the back-propagation algorithm using a training signal. In computer simulations, this filter is shown to be quantitatively effective for both step-like signals and continuous ones, giving totally small mean square error for both signals. Moreover, this filter is shown to be quite effective for face image processing to make the skin look smooth and beautified.
