An Improved Observation Model for Super-Resolution under Affine Motion

TL;DR

This paper introduces an improved observation model for super-resolution that effectively handles non-isometric affine motions, such as those encountered in airborne imaging, leading to better reconstruction quality.

Contribution

The paper extends existing super-resolution observation models to accurately account for affine motion by decomposing it into shear transforms, enhancing performance on variable scale motions.

Findings

Improved super-resolution results on synthetic sequences.

Equivalent performance to existing models on isometric motions.

Better reconstruction quality with non-isometric affine motion.

Abstract

Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher-resolution images. We propose an original observation model devoted to the case of non isometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main observation models used in the SR literature deal with motion, and we explain why they are not suited for non isometric motion. Then, we propose an extension of the observation model by Elad and Feuer adapted to affine motion. This model is based on a decomposition of affine transforms into successive shear transforms, each one efficiently implemented by row-by-row or column-by-column 1-D affine transforms. We demonstrate on synthetic and real sequences that our observation model incorporated in a SR reconstruction technique leads to better results in the case of variable scale motions and it provides equivalent results in the case of isometric motions.