An Efficient and Robust Algorithm for Improving the Resolution of Video Sequences

  • Authors:

  • Yubing Han;Rushan Chen;Feng Shu

  • Affiliations:

  • School of Electronic Engineering & Optoelectronic Techniques, Nanjing University of Science & Technology, Nanjing, China 210094;School of Electronic Engineering & Optoelectronic Techniques, Nanjing University of Science & Technology, Nanjing, China 210094;School of Electronic Engineering & Optoelectronic Techniques, Nanjing University of Science & Technology, Nanjing, China 210094

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
  • Year:
  • 2009

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Abstract

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.