Adaptive De-interlacing algorithm based on Kalman forecast
Junxia Gu · Journal of Circuits and Systems · 2007
An adaptive de-interlacing algorithm based on Kalman forecast is presented in this paper.It consists of 3 modules,the detection of motion blocks,the adaptive motion estimation with Kalman filtering,and the motion compensation for motion blocks and field repetition for static blocks.The motion blocks can be accurately detected by using successive 4-field images.The motion estimation module with Kalman filtering searches motion vector only for motion blocks,and the search model is adaptive to motion velocity and acceleration.Two de-interlacing strategies are adopted to meet the different requirements of motion blocks and static blocks respectively.Compared with full search algorithm,the proposed algorithm greatly reduces the computational cost with keeping the de-interlacing performance approximately.