2 research outputs found
Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
In this paper we introduce a fully end-to-end approach for multi-spectral
image registration and fusion. Our method for fusion combines images from
different spectral channels into a single fused image by different approaches
for low and high frequency signals. A prerequisite of fusion is a stage of
geometric alignment between the spectral bands, commonly referred to as
registration. Unfortunately, common methods for image registration of a single
spectral channel do not yield reasonable results on images from different
modalities. For that end, we introduce a new algorithm for multi-spectral image
registration, based on a novel edge descriptor of feature points. Our method
achieves an accurate alignment of a level that allows us to further fuse the
images. As our experiments show, we produce a high quality of multi-spectral
image registration and fusion under many challenging scenarios
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
In this paper, we introduce a novel deep-learning method to align
cross-spectral images. Our approach relies on a learned descriptor which is
invariant to different spectra. Multi-modal images of the same scene capture
different signals and therefore their registration is challenging and it is not
solved by classic approaches. To that end, we developed a feature-based
approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration
problem. Our algorithm detects corners by Harris and matches them by a
patch-metric learned on top of CIFAR-10 network descriptor. As our experiments
demonstrate we achieve a high-quality alignment of cross-spectral images with a
sub-pixel accuracy. Comparing to other existing methods, our approach is more
accurate in the task of VIS to NIR registration