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Decimation Estimation and Super-Resolution Using Zoomed Observations

  • Prakash P. Gajjar
  • Manjunath V. Joshi
  • Asim Banerjee
  • Suman Mitra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

We propose a technique for super-resolving an image from several observations taken at different camera zooms. From the set of these images, a super-resolved image of the entire scene (least zoomed) is obtained at the resolution of the most zoomed one. We model the super-resolution image as a Markov Random Field (MRF). The cost function is derived using a Maximum a posteriori (MAP) estimation method and is optimized by using gradient descent technique. The novelty of our approach is that the decimation (aliasing) matrix is obtained from the given observations themselves. Results are illustrated with real data captured using a zoom camera. Application of our technique to multiresolution fusion in remotely sensed images is shown.

Keywords

Mean Square Error High Resolution Image Markov Random Field Markov Random Field Model Zoom Factor 
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 2006

Authors and Affiliations

  • Prakash P. Gajjar
    • 1
  • Manjunath V. Joshi
    • 1
  • Asim Banerjee
    • 1
  • Suman Mitra
    • 1
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

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