Data-dependent weighted average filtering for image sequence enhancement

In this study, we consider a filtering method for image sequence degraded by additive Gaussian 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 propose a Video-DDWA filter. The Video-DDWA filter is derived by the following 2 steps. In the first step, 2D-datadependent weighted average (DDWA) filter, whose all weights are decided by local information is extend to 3D-DDWA filter. In the second step, a motion information as the motion detector with robustness for Gaussian noise is taken into the 3D-DDWA filter. In addition to less computational load than the 3D-DDWA filtering with MC, Video-DDWA filtering gives better image sequence restoration results.