An iterative least-square training method for classification-based motion adaptive temporal filtering

This paper presents a motion-adaptive temporal filtering scheme based on trained least-square filtering. A novel iterative method is utilized in the training stage to find the optimal motion-adaptive temporal filter coefficient. The proposed approach shows optimal noise reduction performance in the LMS sense without introducing any blur.