Coupled multi-frame super-resolution with diffusive motion model and total variation regularization

The problem of recovering a high-resolution image from a set of distorted (e.g., warped, blurred, noisy) and low-resolution images is known as super-resolution. Accurate motion estimation from low-resolution measurements is a fundamental challenge of the super-resolution problem. Some recent promisi...

Full description

Saved in:
Bibliographic Details
Main Authors: Ebrahimi, M., Vrscay, E.R., Martel, A.L.
Format: Conference Proceeding
Language:English
Subjects:
High-resolution imaging
Image reconstruction
Image resolution
Image sampling
Interpolation
Layout
Motion estimation
Robustness
Sensor arrays
Spatial resolution
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The problem of recovering a high-resolution image from a set of distorted (e.g., warped, blurred, noisy) and low-resolution images is known as super-resolution. Accurate motion estimation from low-resolution measurements is a fundamental challenge of the super-resolution problem. Some recent promising advances in this area have been focused on coupling or combing the super-resolution reconstruction and the motion estimation. However, the existing approaches are limited to parametric motion models, e.g., affine transformations. In this paper, we shall address the coupled super-resolution problem with a non-parametric motion model. We then consider a variational formulation of the problem and use a PDE-approach to construct a numerical scheme for its solution. In this paper, diffusion regularization is used for the motion model and total variation regularization for the super-resolved image.
ISSN:2166-5559
DOI:10.1109/LNLA.2009.5278403