An efficient and robust super-resolution reconstruction algorithm for video sequences is proposed. In this algorithm, the L1 and L2 norms are introduced to form the data fusion term according to whether there exits motion estimation, and a robust Bilateral-TV regularization term is added to overcome the ill-posed problem of super-resolution estimation. Furthermore, we propose the use of regularization functional instead of a constant regularization parameter. The regularization functional is defined in terms of the reconstructed image at each iteration step, therefore allowing for the simultaneous determination of its value and the reconstruction of the super-resolution image. The iteration scheme, convexity and control parameter are thoroughly studied. Experimental results demonstrate the power of the proposed method. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Han, Y., Chen, R., & Shu, F. (2009). An efficient and robust algorithm for improving the resolution of video sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5553 LNCS, pp. 1054–1062). https://doi.org/10.1007/978-3-642-01513-7_116
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