An Improved Observation Model for Super-Resolution under Affine Motion

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

Multi-algorithm Swath Consistency Detection for Multibeam Echosounder Data

It is unrealistic to expect that any single algorithm for pre-filtering Multibeam Echosounder data will be able to detect all of the “noise in such data all of the time. This paper therefore presents a scheme for fusing the results of many pre-filtering sub-algorithms in order to form one, significantly more robust, meta-algorithm. This principle is illustrated on the problem of consistency detection in regions of sloping bathymetry. We show that the meta-algorithm is more robust, adapts dynamically to sub-algorithm performance, and is consistent with operator assessment of the data. The meta-algorithm is called the Multi-Algorithm Swath Consistency Detector