Temporally adaptive filtering of noisy image sequences using a robust motion estimation algorithm

A motion-compensated noise suppression algorithm that employs temporally adaptive filtering along motion trajectories is proposed for image sequences. Filtering is performed via linear minimum mean square error (LMMSE) point estimation. Motion trajectories are determined using a recent motion estimation algorithm, which is capable of performing very well at low signal-to-noise ratios (SNRs). The results suggest that the proposed method is far superior to methods that incorporate implicit or explicit motion compensation, especially in cases of low SNR and/or significant interframe motion.<<ETX>>

[1]  Sergei Fogel,et al.  The estimation of velocity vector fields from time-varying image sequences , 1991, CVGIP Image Underst..

[2]  Jae Lim,et al.  Implicit motion compensated noise reduction of motion video scenes , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Thomas S. Huang,et al.  Image sequence analysis , 1981 .

[4]  D. Walker,et al.  Improved Pel-Recursive Motion Compensation , 1984, IEEE Trans. Commun..

[5]  A. A. Sawchuk,et al.  Motion compensated enhancement of noisy image sequences , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[6]  Eric Dubois,et al.  Noise Reduction in Image Sequences Using Motion-Compensated Temporal Filtering , 1984, IEEE Trans. Commun..

[7]  D. Boekee,et al.  A pel-recursive Wiener-based displacement estimation algorithm , 1987 .

[8]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.