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.