Thus it's quite common to get better performing impulse noise filters with model-based systems, which are programmed with the time and frequency properties of the noise to remove only impulse obliterated samples.
Model-based systems offer a more effective way to filter out impulse noise in data by focusing on the specific properties of the noise itself.  This targeted approach leads to improved filtering performance compared to traditional methods.