Data-dependent weighted median filtering with robust motion information for restoring image sequence degraded by additive Gaussian and impulsive noise

In this study, we consider a filtering method for image sequence degraded b y additive Gaussian noise and/or impulse noise (i.e., mixed noise). For rem oving the mixed noise from the 1D/2D signal. weighted median filters are we ll known as a proper choice. We have also proposed a filtering tool based o n the weighted median filter with a data-dependent method. We call this dat a-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough perform ance to restore the image sequence degraded by the noise. The reason is tha t the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawba ck, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some sim ulations, the Video-DDWM filter is shown to give effective restoration resu lts than that given by the DDWM filtering and the conventional filtering me thod with a motion-conpensation (MC).