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Locally Adaptive Regularization for Robust Multiframe Super Resolution Reconstruction

  • S. Chandra Mohan
  • K. Rajan
  • R. Srinivasan
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

Super resolution reconstruction (SRR) is a post processing technique to correct the degradation of the acquired images due to warping, blur, downsampling and noise. In this paper, image is modeled as Markov random field (MRF) and we propose fuzzy logic filter based on gradient potential (FL) to distinguish between edge and noisy pixels. Based on pixel classification, Tikhonov regularization (TR) or bilateral total variation (BTV) is adopted as a prior in maximum a posteriori (MAP) estimation. Such priors are imperative to obtain a stable solution. Tukey’s biweight norm (TBN) is adopted for removing the outliers. The proposed approach is demonstrated on standard test images. Experimental results indicate that the proposed approach performs quite well in terms of visual evaluation and quantitative measurements.

Keywords

High Resolution Image Tikhonov Regularization Super Resolution Noisy Pixel Bicubic Interpolation 
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 GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • S. Chandra Mohan
    • 1
    • 3
  • K. Rajan
    • 2
  • R. Srinivasan
    • 3
  1. 1.Dept of IAPIIScBangaloreIndia
  2. 2.Dept. of PhysicsIIScBangaloreIndia
  3. 3.ADE, DRDOBangaloreIndia

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