Multichannel regularized iterative restoration of motion compensated image sequences

Restoration of image sequences is an important problem that can be encountered in many processing applications, such as visual communications, robot guidance, and target tracking. The independent restoration of each frame in an image sequence is a suboptimal approach because the between-frame correlations are not explicitly taken into consideration. In this paper, we address this problem by proposing a multichannel restoration approach. The multiple time frames (channels) of the image sequence are restored simultaneously by using a multichannel regularized least-squares formulation of the problem. The regularization operator captures both within and between-frame (channel) properties of the image sequence with the explicit use of the displacement vector field. We propose a number of different approaches to obtain the multichannel regularization operator as well as an algorithm to iteratively compute the restored images. We present experiments that demonstrate beyond any doubt that the proposed multichannel approach produces significantly better restored images compared with the independent frame-by-frame restoration of the image sequence.