Journal of Scientific Computing

, Volume 37, Issue 3, pp 367–382 | Cite as

Image Super-Resolution by TV-Regularization and Bregman Iteration

  • Antonio MarquinaEmail author
  • Stanley J. Osher


In this paper we formulate a new time dependent convolutional model for super-resolution based on a constrained variational model that uses the total variation of the signal as a regularizing functional. We propose an iterative refinement procedure based on Bregman iteration to improve spatial resolution. The model uses a dataset of low resolution images and incorporates a downsampling operator to relate the high resolution scale to the low resolution one. We present an algorithm for the model and we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme and quality of the results.


Super-resolution Total variation restoration Bregman iteration Downsampling Edge preserving 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Departamento de Matematica AplicadaUniversidad de ValenciaBurjassotSpain
  2. 2.Department of MathematicsUniversity of California Los AngelesLos AngelesUSA

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