A Closed Form Algorithm for Superresolution

  • Marcelo O. Camponez
  • Evandro O. T. Salles
  • Mário Sarcinelli-Filho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

Superresolution is a term used to describe the generation of high-resolution images from a sequence of low-resolution images. In this paper an algorithm proposed in 2010, which gets superresolution images through Bayeasian approximate inference using a Markov chain Monte Carlo (MCMC) method, is revised. From the original equations, a closed form to calculate the high resolution image is derived, and a new algorithm is thus proposed. Several simulations, from which two results are here presented, show that the proposed algorithm performs better, in comparison with other superresolution algorithms.

Keywords

Markov Chain Monte Carlo Discrete Fourier Transform Gaussian Markov Random Field Superresolution Image Discrete Fourier Transform Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcelo O. Camponez
    • 1
  • Evandro O. T. Salles
    • 1
  • Mário Sarcinelli-Filho
    • 1
  1. 1.Graduate Program on Electrical EngineeringFederal University of Espirito SantoVitóriaBrazil

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