Linear models for multiframe super-resolution restoration under nonaffine registration and spatially varying PSF

Sean Borman; Robert L. Stevenson

doi:10.1117/12.538315

21 May 2004 Linear models for multiframe super-resolution restoration under nonaffine registration and spatially varying PSF

Sean Borman, Robert L. Stevenson

Author Affiliations +

Proceedings Volume 5299, Computational Imaging II; (2004) https://doi.org/10.1117/12.538315
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Multi-frame super-resolution restoration refers to techniques for still-image and video restoration which utilize multiple observed images of an underlying scene to achieve the restoration of super-resolved imagery. An observation model which relates the measured data to the unknowns to be estimated is formulated to account for the registration of the multiple observations to a fixed reference frame as well as for spatial and temporal degradations resulting from characteristics of the optical system, sensor system and scene motion. Linear observation models, in which the observation process is described by a linear transformation, have been widely adopted. In this paper we consider the application of the linear observation model to multi-frame super-resolution restoration under conditions of non-affine image registration and spatially varying PSF. Reviewing earlier results, we show how these conditions relate to the technique of image warping from the computer graphics literature and how these ideas may be applied to multi-frame restoration. We illustrate the application of these methods to multi-frame super-resolution restoration using a Bayesian inference framework to solve the ill-posed restoration inverse problem.

Citation Download Citation

Sean Borman and Robert L. Stevenson "Linear models for multiframe super-resolution restoration under nonaffine registration and spatially varying PSF", Proc. SPIE 5299, Computational Imaging II, (21 May 2004); https://doi.org/10.1117/12.538315

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available