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Image Super-Resolution by TV-Regularization and Bregman Iteration

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Abstract

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.

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Authors and Affiliations

  1. Departamento de Matematica Aplicada, Universidad de Valencia, C/ Dr Moliner, 50, 46100, Burjassot, Spain

    Antonio Marquina

  2. Department of Mathematics, University of California Los Angeles, Los Angeles, CA, 90095, USA

    Stanley J. Osher

Authors
  1. Antonio Marquina
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  2. Stanley J. Osher
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Correspondence to Antonio Marquina.

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Marquina, A., Osher, S.J. Image Super-Resolution by TV-Regularization and Bregman Iteration. J Sci Comput 37, 367–382 (2008). https://doi.org/10.1007/s10915-008-9214-8

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  • DOI: https://doi.org/10.1007/s10915-008-9214-8

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