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A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution)

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Abstract

Superresolution produces high-quality, high-resolution images from a set of degraded, low-resolution images where relative frame-to-frame motions provide different looks at the scene. Superresolution translates data temporal bandwith into enhanced spatial resolution. If considered together on a reference grid, given low-resolution data are nonuniformly sampled. However, data from each frame are sampled regularly on a rectangular grid. This special type of nonuniform sampling is called interlaced sampling. We propose a new wavelet-based interpolation-restoration algorithm for superresolution. Our efficient wavelet interpolation technique takes advantage of the regularity and structure inherent in interlaced data, thereby significantly reducing the computational burden. We present one- and two-dimensional superresolution experiments to demonstrate the effectiveness of our algorithm.

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Authors and Affiliations

  1. Scientific Computing and Computational Mathematics Program, Standrod University, 94305-9025, Stanford, California, USA

    Nhat Nguyen

  2. Department of Electrical Engineering, University of California, 95064, Santa Cruz, California, USA

    Peyman Milanfar

Authors
  1. Nhat Nguyen
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  2. Peyman Milanfar
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Additional information

This work was supported in part by the National Science Foundartion Grant CCR-9984246.

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Nguyen, N., Milanfar, P. A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution). Circuits Systems and Signal Process 19, 321–338 (2000). https://doi.org/10.1007/BF01200891

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  • DOI: https://doi.org/10.1007/BF01200891

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