Image Super-Resolution by TV-Regularization and Bregman Iteration

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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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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Keywords

  • Super-resolution
  • Total variation restoration
  • Bregman iteration
  • Downsampling
  • Edge preserving