Super-Resolution Reconstruction of Image Sequence Using Multiple Motion Estimation Fusion

Super-resolution reconstruction algorithm produces a high-resolution image from a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME) are essential for the whole restoration. In this paper, a new super-resolution reconstruction algorithm is developed using a robust ME method, which fuses multiple estimated motion vectors within the sequence. The new algorithm has two major improvements compared with the previous research. First, instead of only two frames, the whole sequence is used to obtain a more accurate and stable estimation of the motion vector of each frame; second, the reliability of the ME is quantitatively measured and introduced into the cost function of the reconstruction algorithm. The algorithm is applied to both synthetic and real sequences, and the results are presented in the paper. This work is supported by the National Natural Science Foundation of China under Grant No. 60302007. Cheng Wang is currently a Ph.D. candidate in the ATR Lab, School of Electronic Science and Engineering, National University of Defense Technology. His research interests include image understanding and fusion. Run-Sheng Wang is a professor of School of Electronic Science and Engineering, National University of Defense Technology. His research interests include image analysis, image understanding, and information fusion.