Statistical performance modeling for superresolution: a discrete data-continuous reconstruction framework
JOSA A, Vol. 26, Issue 7, pp. 1730-1746 (2009)
http://dx.doi.org/10.1364/JOSAA.26.001730
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Abstract
We address performance modeling of superresolution (SR) techniques. Superresolution consists in combining several images of the same scene to produce an image with better resolution and contrast. We propose a discrete data-continuous reconstruction framework to conduct SR performance analysis and derive a theoretical expression of the reconstruction mean squared error (MSE) as a compact, computationally tractable function of signal-to-noise ratio (SNR), scene model, sensor transfer function, number of frames, interframe translation motion, and SR reconstruction filter. A formal expression for the MSE is obtained that allows a qualitative study of SR behavior. In particular we provide an original outlook on the balance between noise and aliasing reduction in linear SR. Explicit account for the SR reconstruction filter is an original feature of our model. It allows for the first time to study not only optimal filters but also suboptimal ones, which are often used in practice.
© 2009 Optical Society of America
OCIS Codes
(100.2000) Image processing : Digital image processing
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(100.6640) Image processing : Superresolution
ToC Category:
Image Processing
History
Original Manuscript: February 5, 2009
Revised Manuscript: May 20, 2009
Manuscript Accepted: May 20, 2009
Published: June 25, 2009
Citation
Frédéric Champagnat, Guy Le Besnerais, and Caroline Kulcsár, "Statistical performance modeling for superresolution: a discrete data-continuous reconstruction framework," J. Opt. Soc. Am. A 26, 1730-1746 (2009)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-26-7-1730
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