Data-dependent weighted median filtering with robust motion information for image sequence restoration

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mined noise). In general, for the image sequence filtering, motion compensation (MC) method is required in order to obtain good filtering performance both in the still and moving regions of an image sequence. Nevertheless a heavy computation load is imposed on MC method and MC tends to get mistaken motion vector owing to additive noise. To overcome above drawbacks of MC, we have proposed a Video-Data Dependent Weighted Average (Video-DDWA) filter for image sequence restoration degraded by additive Gaussian noise. The Video-DDWA filter whose weights are controlled by some local information contain a motion information as a motion detector is shown that the motion information method is more effective tool than MC method for image sequence restoration. However Video-DDWA filter is not proper for removing the mixed noise. Therefore, we replace weighted average filters and a motion information of the Video-DDWA with weighted median filters and a mixed noise robust motion information, respectively. We propose this filter as a Video-Data Dependent Weighted Median (Video-DDWM) filter for removing mixed noise from image sequence. Through some simulations, the Video-DDWM filter is proven to be more effective both the restoration results and computation time than the 3D-DDWM filter with impulse robust MC for removing mixed noise from image sequence.