Thermal and Visible Image Registration Using Deep Homography

EasyChair Preprint 8248

Abstract

Fusing thermal and visible images is a recurring challenge in computer vision, especially when the images of the two modalities are not well registered. This registration problem is traditionally solved by matching descriptors and depends on the richness and discriminating power of the representation. Ensuring that detected features are dense and uniformly distributed is not necessarily guaranteed. More recently, machine learning methods addressed the issue of visible to visible matching, but few address the multi-modality setting. In this paper, we propose to address the special case of thermal-visible image registration with small baseline parallax correction. Our deep homography model is evaluated on an open thermal and visible dataset with two training settings, unsupervised and supervised. Results demonstrate the feasibility of the approach, and performances comparison to state-of-the-art models is evaluated.

Keyphrases: deep homography, deep learning, image fusion, parallax correction, thermal and visible fusion

@booklet{EasyChair:8248,
  author    = {Benoit Debaque and Hughes Perreault and Jean-Philippe Mercier and Marc-Antoine Drouin and Rares David and Benedicte Chatelais and Nicolas Duclos-Hindie and Simon Roy},
  title     = {Thermal and Visible Image Registration Using Deep Homography},
  howpublished = {EasyChair Preprint 8248},
  year      = {EasyChair, 2022}}