Center weighted median filtering and related nonlinear filtering techniques with applications in image enhancement.

摘要

The center weighted median (CWM) filter, which is a weighted median filter giving more weight only to the central value of each window, is investigated. This filter can preserve image details while suppressing additive white and/or impulsive-type noise. The statistical properties of the CWM filter are analyzed. It is shown that the CWM filter can outperform the median filter, while its implementation is almost as simple as median filtering. Some relationships between CWM and other median-type filters, such as the Winsorizing smoother and the multi-level median filter, are derived. In an attempt to improve the performance of CWM filters, an adaptive CWM (ACWM) filter having space varying central weight is proposed. We show that the ACWM filter is an excellent detail-preserving smoother that can suppress signal-dependent noise as well as signal-independent noise.In addition to the investigation of CWM and ACWM filters, a nonlinear recursive filter for the suppression of impulsive noise is studied. This filter selects from each window a sample closest in value to the most recent output and is thus named the last output reference (LOR) filter. A relationship between the LOR and recursive median filters is derived, and some statistical properties are studied through computer simulations. The results indicate that this filter preserves edges while suppressing impulsive noise. It is shown that LOR filters are more effective in suppressing impulses, and are often simpler to implement than median filters.