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In this paper, we propose a multichannel regularized recovery approach to ameliorate coding artifacts in compressed video. The major advantage of the proposed approach is that both temporal and spatial correlations in a video sequence can be exploited to complement the compressed video data. In particular, a temporal regularization term is introduced to enforce smoothness along the motion trajectories defined by the transmitted motion vectors for motion compensation. Several forms of temporal regularization with different computational complexity are considered. Based on the proposed approach, recovered images are obtained from the compressed data using the well-known gradient-projection algorithm. Moreover, an iterative algorithm is proposed for the determination of regularization parameters at the coder side. A number of numerical experiments using several H.261 and H.263 compressed streams are presented to evaluate the performance of the proposed recovery algorithms. Results from these experiments demonstrate that the use of temporal regularization can yield significant improvement in the quality of the recovered images-in terms of both visual evaluation and objective peak-signal-to-noise (PSNR) measure. ommended by Associate Editor V. Anastassopoulos.M. G. Choi is with

The recent advances in visual communications make restoration of image sequences an increasingly important problem. In addition, this problem finds applications in other fields such as robot guidance and target tracking. Restoring the individual frames of an image sequence independently is a suboptirnal approach because the between frame relations of the image sequence are not explicitly incorporated into the restoration algorithm. In this paper we address this problem by proposing a family of rnultichannel algorithms that restore the multiple time frames (channels) simultaneously. This is accomplished by using a multichannel regularized formulation in which the regularization operator captures both within and between-frame (channel) properties of the image sequence. More specifically, this operator captures both the spatial within-frame smoothness and the temporal along the direction of the motion between-frame smoothness. We propose a number of different methods to define multichannel regularization operators and a family of algorithms to iteratively obtain the restored images. We also present experiments that demonstrate beyond any doubt that the proposed approach produces significant improvements over traditional independent frame restoration of image sequences.

It has been demonstrated that multichannel restorat i o n of m o t i o n compensated images sequences is very effective w h e n good estimates of the displacement vect o r field (DVF) are available. In this paper two new stochastic algorithms f o r template-matching based DVF estimation are proposed. These algorithms are based o n the relationship of the template-matching and the image restoration problems. Experiments are shown where the value of the proposed D V F estimation algorithms is demonstrated.