MISFIT-V: Misaligned Image Synthesis and Fusion using Information from
  Thermal and Visual
Aadhar Chauhan Isaac Remy Danny Broyles Karen Leung
Detecting humans from airborne visual and thermal imagery is a fundamental
challenge for Wilderness Search-and-Rescue (WiSAR) teams, who must perform this
function accurately in the face of immense pressure. The ability to fuse these
two sensor modalities can potentially reduce the cognitive load on human
operators and/or improve the effectiveness of computer vision object detection
models. However, the fusion task is particularly challenging in the context of
WiSAR due to hardware limitations and extreme environmental factors. This work
presents Misaligned Image Synthesis and Fusion using Information from Thermal
and Visual (MISFIT-V), a novel two-pronged unsupervised deep learning approach
that utilizes a Generative Adversarial Network (GAN) and a cross-attention
mechanism to capture the most relevant features from each modality.
Experimental results show MISFIT-V offers enhanced robustness against
misalignment and poor lighting/thermal environmental conditions compared to
existing visual-thermal image fusion methods.