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Super-resolution Image Reconstruction

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Super-resolution Image Reconstruction Sina Jahanbin Richard Naething EE381K-14 March 10, 2005 Problem Statement There is a limit to the spatial resolution that can be ... – PowerPoint PPT presentation

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Title: Super-resolution Image Reconstruction


1
Super-resolution Image Reconstruction
  • Sina Jahanbin
  • Richard Naething
  • EE381K-14
  • March 10, 2005

2
Problem Statement
  • There is a limit to the spatial resolution that
    can be
  • recorded by any digital device. This may be due
    to
  • optical distortions
  • motion blur
  • under-sampling
  • noise

3
Introduction to Super-resolution (SR)
Reconstruction Techniques
  • SR image reconstruction is the process of
    combining several low resolution (LR) images into
    a single higher resolution (HR) image.

4
Restoration of a Single Superresolution Image
from Several Blurred, Noisy, and Undersampled
Measured ImagesElad Feuer, 1997
  • Three main tools in single image restoration
  • Maximum likelihood (ML) estimator
  • Maximum a posteriori (MAP)
  • Projection onto convex sets (POCS)
  • This paper takes these existing single image
    restoration techniques and applies them to SR
  • A hybrid algorithm has been proposed that
    combines the ML estimator and POCS

5
Superresolution Video Reconstruction with
Arbitrary Sampling Lattices and Nonzero Aperture
Time Patti, Sezan, Murat, 1997
  • Uses a model that takes into account details
    ignored by previous SR models
  • Arbitrary sampling lattice
  • Sensor elements physical dimensions
  • Aperture time
  • Focus blurring
  • Additive noise

6
Limits on Super-Resolution and How to Break
Them Baker Kanade, 2002
  • Assumes image registration has already been
    accomplished and focuses on fusing step or
    combining multiple aligned LR images into HR
    image
  • Uses what the authors call a hallucination or
    recogstruction algorithm
  • Claims significantly better results both
    subjectively and in RMS pixel error

7
Future Work
  • Many papers on SR base their results on
    subjective viewing of images or use an objective
    measurement, such as RMS, that in many
    applications is not meaningful.
  • We propose to develop an objective measure of SR
    methods that has a basis in real world
    application performance.
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