Abstract: A method of temporally adaptive filtering of the frames of an image sequence is disclosed. To filter the kth frame, first, the motion trajectories that transverse the pixel locations at the kth frame are determined using a motion estimation algorithm. Motion trajectories pass through a predetermined number of frames neighboring the kth frame. Image values at the neighboring frames, along motion trajectories are then evaluated. Temporally adaptive linear minimum mean square error (LMMSE) estimates of the image values at the pixel locations of the kth frame are determined using the image values along the motion trajectories. These steps are repeated for each of the other frames of the image sequence.

Abstract: A method for performing multiframe Wiener restoration of noisy and blurred image sequences that provides either a cross-correlated multiframe Wiener restoration or a motion-compensated multiframe Wiener restoration. The cross-correlated multiframe restoration uses interframe correlations explicitly through the estimated cross-power spectra of the frames, and is computationally efficient in its calculation of the Wiener estimate by using either a closed form solution or using a small-size matrix inversion. The motion-compensated multiframe Wiener restoration is used when the interframe motion is a global shift which is either known a priori, or estimated using a motion estimation algorithm. The motion-compensated multiframe Wiener filter has a closed-form solution and does not require any matrix inversion.