title: Statistical performance modeling for superresolution: a discrete data-continuous reconstruction framework.
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.
authors: Office National d'Etudes et de Recherches Aérospatiales (ONERA), Chatillon Cedex, France. Frederic.Champagnat@onera.fr
authors: Frédéric
authors: F
authors: Champagnat
authors: Guy
authors: G
authors: Le Besnerais
authors: Caroline
authors: C
authors: Kulcsár