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Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm

  • Hui Ji
  • Cornelia Fermüller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)

Abstract

We present a theoretical analysis and a new algorithm for the problem of super-resolution imaging: the reconstruction of HR (high-resolution) images from a sequence of LR (low-resolution) images. Super-resolution imaging entails solutions to two problems. One is the alignment of image frames. The other is the reconstruction of a HR image from multiple aligned LR images. Our analysis of the latter problem reveals insights into the theoretical limits of super-resolution reconstruction. We find that at best we can reconstruct a HR image blurred by a specific low-pass filter. Based on the analysis we present a new wavelet-based iterative reconstruction algorithm which is very robust to noise. Furthermore, it has a computationally efficient built-in denoising scheme with a nearly optimal risk bound. Roughly speaking, our method could be described as a better-conditioned iterative back-projection scheme with a fast and optimal regularization criteria in each iteration step. Experiments with both simulated and real data demonstrate that our approach has significantly better performance than existing super-resolution methods. It has the ability to remove even large amounts of mixed noise without creating smoothing artifacts.

Keywords

Iterative Reconstruction Tikhonov Regularization Total Variation Regularization Wavelet Denoising Mixed Noise 
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

  • Hui Ji
    • 1
  • Cornelia Fermüller
    • 1
  1. 1.Center for Automation Research, Department of Computer ScienceUniversity of MarylandCollege ParkUSA

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